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ARK: Survival Evolved Console Commands – Cheat Codes and More in 2023

Make Dinosaurs yours with these awesome ARK: Survival Evolved console commands.

When it comes to survival, Dinosaurs probably aren’t the most prominent experts on the subject, going by how many of them are still around these days. That hasn’t stopped ARK: Survival Evolved from becoming an incredibly popular title that mixes the crafting/survival elements of Minecraft with some of the most dangerous giant lizards that have ever roamed the planet. As with many survival and crafting titles, there is as much content in ARK as you can come up with, since no two games are the same. That doesn’t mean that you won’t get bored of t all eventually, which is where console commands could come to the rescue.

The console commands we’ve found can do crazy things, from making yourself invincible to spawning in an infinite supply of crafting and survival resources. There is an insanely high number of different things that you can do with these commands, so we’ve compiled them into several group to make them easier for you to find.

How to Activate the Console

First things first, how you open the console changes depending on which platform you happen to be playing the game on. For instance, on PC, you press Tab to open the console, while on Stadia you press ` underneath Escape. If you’re on a console, hit the north and west face buttons and the shoulder buttons (LB+RB or R1+L1) at the same time. This should open up the command console so you can type in one of the commands that we’ve listed below.

If you’re playing multiplayer, you’ll also need to type ‘enablecheats’ into the command console or most of the codes below won’t work. If a command from the list below still isn’t working for you, try prefacing it with either ‘cheat’ or ‘admincheat’ and see if that fixes the issue. For example:

cheat fly

ARK: Survival Evolved Useful Console Commands

Since there are so many commands in the game, we’ve assembled some of the most useful ones into the categories that you’ll find below.

ARK: Survival Evolved Item Console Commands

The command for spawning items in ARK: Survival Evolved is a little more complicated than most games. The reason for this complexity is that spawning items isn’t a single command; it’s actually three separate commands that are each used in different ways. Fortunately, we can mostly ignore two of them and instead only use the below command as it is the easiest to understand:

GiveItemNum [Item ID] [Amount] [Quality] [Blueprint y/n]

The first part of the command should be pretty self-explanatory, as it’s the part that actually tells the game we want to spawn something. Each in-game item has an ID that you can use to spawn it via the console, like many other games. We’ve listed the most important ones below, but you can find all of them on the game’s official wiki. The ‘Amount’ heading is simply how many of the items you want to spawn, and Quality refers to the overall quality of the item measured in 1-100. The final modifier is for blueprints and is either a 1 or 0. If set to a 1, you will receive the blueprint version instead of the actual item.

Item NameItemIDSimple Pistol1Simple Bullet4Assault Rifle 2Advanced Rifle Ammo242Cooked Meat14Filled Canteen375Medical Brew52Wood7Metal Ingot73Hide10

ARK: Survival Evolved Teleportation Console Commands

Whether you’re playing ARK on your own or with a group of your friends, teleportation can be an incredibly useful tool. As well as being able to teleport players to you and vice-versa, you can also use these commands to travel vast distances over the land. Bear in mind, to find your playerID you’ll need to be an admin on the server and use the showmyadminmanager command to get a list of different player IDs.

Command EffectConsole CommandTeleports you forward until you hit somethingteleportTeleports [PlayerID] to youteleportplayeridtome [PlayerID]Teleports [PlayerName] to youteleportplayernametome [PlayerName]Teleports you to [PlayerID] teleporttoplayerid [PlayerID]Teleports you to [PlayerName]teleporttoplayername [PlayerName]

ARK: Survival Evolved Cheat Console Commands

Once you’re done trying out the item spawning and teleportation commands above, you can try these ones below that are a bit more like classical cheats. Bear in mind, that these will have a huge impact on your gameplay experience. If you’re after a challenge, be careful which commands you use from the list below.

Command EffectConsole CommandMakes the player fly flyEnables God Mode (no damage)SetGodMode TrueDisables God Mode SetGodMode FalseGives you god mode, Infinite Stats, and Makes you Invisible to enemiesLeaveMeAloneFills all your stats and keeps them full, also infinite ammo when shootingInfiniteStatsKills the creature that you’re currently aiming your crosshair atKillDestroys all non-player entities on the map, including tamed creatures. DestroyAllEnemies

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Best Laptop For Writers, Journalists, Bloggers, And More In 2023

Best Laptop For Writers, Journalists, Bloggers, and More in 2023

Top recommendations for the best portable typing experience

Tom Bardwell

Choosing the best laptop for writing is key to the word-weaving work of writers. While most may think a pen and paper will do, the reality is modern writers have a particular set of needs that need to be met to bring words to life.

Whether you are a budding scribe or seasoned journalist, a laptop with a smooth, responsive keyboard, plenty of battery life, and a form factor suitable for home and outdoor use is crucial. To that end, we gathered together recommendations for the best laptops for writers currently out there.

Products at a Glance

How we picked the best laptop for writers

Trawling for laptops for writers is different from hunting down, say, a gaming laptop. The list of criteria steers far more to the practical than simply searching for the most decked-out option.

We relied particularly on more tangible factors such as construction, design, battery life, and keyboard quality and feel. We also factored in hardware specifications, price, and operating system. Screen size and the quality of the display also played a big part – laptops with bright screens, equally suitable for indoor and outdoor use, that doesn’t strain the eyes scored extra points.

In the end, we narrowed our search down to the five laptops that, in our estimation, are great options for writers looking to upgrade their primary writing tool. We’ve included both Chrome OS and Windows options, as well as a variety of sizes and price points. Read on to find a review of each alongside quick pros and cons. Let’s get into it.

Our Recommended

Best Laptop For Writers, Journalists, Bloggers, and More in 2023

1

PROS

12 hour battery life

Silent, comfortable, and responsive ‘Hush’ keys

Good hardware at this price point

Top-end construction and sleek design

CONS

Cost can rise fast for the highest spec model

The Google Pixelbook Go is an ultra-compact, ultra-lightweight 13 mm thin, 2.3 lb Chromebook that should suit writers already loyal to Google’s suite of applications as well as those that want a stylish and portable workhorse laptop at a very reasonable price point.

The Google Pixelbook Go hides ample power under its sleek, refined, finely painted magnesium outer shell and sturdy construction. There’s plenty of scope for customization here with CPU options ranging from an Intel Core m3 to a Core i7, RAM up to 16 GB, SSD storage up to 256 GB, a high-quality 1080p webcam, Wi-Fi, Bluetooth, an extra-large etched glass trackpad, and a beautiful 13.3-inch LCD touch screen display – there’s even a high-end model that features a 4K Ultra HD Molecular Display. However, you’ll pay considerably more for the pleasure.

The full-size backlit keyboard marries both silent operations with satisfying bounce back and tactile feel thanks to Google’s proprietary ‘Hush’ keys. Typing away for extended periods is a genuine pleasure, and those looking for an excellent keyboard at a reasonable price should strongly consider the Google Pixelbook Go.

Great battery life is another draw of the Google Pixelbook Go. A single charge delivers an astounding 12 hours of power, and through quick charging, the laptop can scoop up two hours of battery life for 20 minutes of charging.

If you don’t mind Chrome OS’s limitations and prize typing comfort above anything else, then the Google Pixelbook Go comes highly recommended.

2

PROS

Beautiful 4K UHD touch screen

Great specifications and performance

12 to 17-hour battery life

Comfy keyboard

Sturdy construction and sharp looks

CONS

Expensive

Alongside portability, flexibility is a top requirement for writers hunting down their next laptop. In that sense, the HP Spectre x360 is a strong contender for the best laptop for writers and, without a doubt, the best 2-in-1 laptop for wordsmiths. With, as the name suggests, a well-designed 360-degree hinge, the HP Spectre x360 converts from laptop to tablet mode smoothly and quickly, adapting to any situation with ease.

Constructed from a single sheet of precision CNC machined aluminum, the HP Spectre x360’s build quality is nothing short of incredible, lightweight, compact, and looks sharp with a nightfall black and copper-accented color scheme (others are available from Poseidon blue to natural silver).

The HP Spectre x360 is available in a variety of configurations. For a balance between good performance and price point, we favor the one that features an Intel Core i7-10510U, 16 GB of RAM, 512 GB SSD, NVIDIA GeForce MX330 GPU, Wi-Fi 6, Bang & Olufsen audio, and a bunch of useful I/O ports.

The keyboard is also worth mentioning, with on-point spacing between the well-sized backlit keys and a perfectly tuned feel with just the right amount of force and tactile feedback. This is one you’ll easily spend hours using without discomfort or strain. With writing in mind, we are also impressed by the beautiful, near-borderless, anti-reflective 15-inch 4K UHD IPS touch screen that produces vivid colors, deep blacks, and plenty of brightness even in outdoor settings.

While there’s plenty great about the HP Spectre x360, the price leans towards the expensive, especially for the higher-end configurations.

3

PROS

Beautiful construction and design

Gorgeous 4K UHD+ display

Superb performance

Quality keyboard

CONS

Pricey

Poor webcam

On the heavy side

Another top contender for the best laptop for writers is the beautifully constructed and designed Dell XPS 15 with a premium CNC machined aluminum chassis that oozes elegance. It’s one for those that want to write in style with plenty of juice under the hood.

Numerous configurations are possible. We particularly liked the Intel Core i7-10750H, NVIDIA GeForce GTX 1650 TI, 32 GB of DDR4 RAM, 1 TB NVMe SSD, Wi-Fi 6, Bluetooth 5.0, and 86 WHr battery version. That’s because the desktop-class hardware and performance make it a viable substitute/replacement for a full desktop PC, expanding functionality to modest gaming and creative tasks. It comes at a price, it has to be said, but every dollar is well spent in our opinion.

The Dell XPS 15 sports a fantastic backlit keyboard with large, generously-spaced keycaps with 1.3 mm travel and responsive tactile feedback. Coupled with this quality keyboard are a spacious trackpad and soft-touch carbon fiber palm rest. Combined, all these features deliver a premium typing and user experience.

Another feature worth mentioning is the gorgeous 15.6-inch 4K UHD+ display. With its 500-nit brightness, anti-reflective coating, HDR and Dolby Vision support, tiny InfinityEdge bezels, and 92.9% screen-to-body ratio, the Dell XPS 15 easily offers one of the best laptop displays around. Colors are sharp and vivid, blacks are convincingly dark, and the detail is, frankly, terrific.

Compared to other laptops in its class, the Dell XPS 15 is a little on the heavy side but still incredibly portable. Battery life isn’t the best out there, chiefly due to the high-specifications, but you’ll easily get 8 to 10 hours of regular use per charge. Similarly, the 720p webcam is a little lackluster, although plenty suitable for Zoom calls and the like.

4

PROS

13-inch laptop

Solid battery life

Top-notch keyboard experience

Affordable

CONS

I/O offering a little thin

The Microsoft Surface Laptop 3 successfully combines a sleek, slim form factor with excellent specifications, all while offering solid battery life and rugged, long-lasting construction at a price well below rival machines.

Under the hood, Microsoft has a few different configurations that differ mainly on what CPU, RAM count, and SSD size you opt for. Aiming for value, we’d recommend the Intel Core i5-1035G7, 8GB of RAM, 256 GB SSD, and Intel Iris Plus Graphics configuration as it keeps the price below $1000.

The keyboard is unquestionably a highlight. Despite the 13-inch build, the key spacing is generous with just the right amount of key travel and tactile feedback. For us, there’s nothing Microsoft could have done to improve the typing experience – it’s one the best out there regardless of price. There are options too: either a cloth-like Alcantara or aluminum keyboard deck, available depending on the color scheme you opt for.

5

MacBook Air 2023 (M2)

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PROS

Stunning sleek design

Great battery life

Portable

CONS

Limited to two USB-C ports

Only supports one external screen

Lastly, we’ll round off our recommendations for the best laptops for writers with an Apple option – the MacBook Air 2023. Aside from the other Windows options, the MacBook Air is one of the best all-rounder laptops out there. This fan-favorite model matches our other offerings in terms of price while providing exceptional specifications alongside a slim, compact, and comfortable design.

The specification is perfect for any writer. There’s more than enough to power everyday use and cover writing tasks in all their forms: Apple M2 chip, 8-core CPU, 100GB/s memory bandwidth, 13.6″ liquid retina display, USB-C ports, 8GB RAM, and up to 2TB storage.

The Apple MacBook is nothing short of exceptional. The battery life is stacked offering up to 15 hours of wireless website browsing. With its incredibly thin design, great speakers, and lightweight build, this is another laptop to seriously consider.

Things To Consider

Here are the top specs you should consider:

Keyboard Quality and Comfort

The keyboard is often a minor consideration for the average user browsing the web and bingeing their favorite shows. But for writers who spend hours bringing their ideas to life through the written word, a comfortable keyboard that delivers satisfying and responsive keypresses makes all the difference.

While the problem can easily be circumvented by plugging in a USB keyboard, especially if you prefer the feel of mechanical switches, this isn’t convenient for those that like to take their writing on the move.

If possible, we suggest trying out a laptop before committing to a purchase to ensure the keyboard fits your typing style and won’t cause strain hours deep into a prolific writing session.

Battery Life

Writers favor laptops because they are the digital equivalent of a notebook or writing pad – portable, easy to pull out on a whim when inspiration strikes even at the most inconvenient of times, and durable, able to withstand trips to the coffee shop, the park, and shared work spaces.

To that end, battery life is, alongside the keyboard, one of the most important factors of a laptop fit for a writer. In general, non-gaming laptops tend to offer respectable battery efficiency with at least six to eight hours between charges on average, while higher-end options top out at 12 hours.

Whether your favorite writing haunts don’t have a power outlet at hand or inspiration comes to you in the great outdoors, the last thing you want is an ominous pop-up warning of an imminent shutdown. We urge you to spend a little more, if your budget allows it, for better battery life – you’ll thank yourself in the long run.

Screen Size

Your screen size dictates the size of the laptop and keyboard, so you’ll need to weigh up whether you prefer more on-screen real estate to write and check research materials or a smaller footprint, so the laptop easily slips into a satchel. It’s a balance between efficiency and portability.

As a happy middle ground, we favor 15-inch options: plenty of on-screen space to view all your text, but small enough for easy transport. 13-inch laptops are ultra-compact and lightweight, but if you’re a writer that prefers to see the written page in its entirety rather than a paragraph or two at a time, they may stunt your workflow.

17-inch laptops fit for writing do exist and are great for writers that have a fixed writing spot in a home office and aren’t often on the move, in which case, you may want to consider a desktop PC.

Operating System

The best operating system comes down to personal preference. Your preferred OS will largely dictate your laptop options.

macOS is smooth, efficient, and incredibly easy to use and navigate with few crashes. Mac laptops offer a premium experience but come at a substantial premium, not necessarily something a struggling, destitute writer can afford.

Chrome OS, Google’s Chrome browser-based OS, primarily operates alongside an internet connection and is an excellent option for those already invested in the Google ecosystem (Google Docs, Gmail, etc.) and have a constant Wi-Fi signal they can rely on. They generally sell for much cheaper than their Mac and Windows counterparts.

Last but not least, we have the most popular – Windows. This one needs no introduction and should be the OS of choice for those that want versatility and the broadest selection of laptops to choose from.

FAQ: Windows, Google or Mac?

All operating systems are excellent choices and the choice should come down to familiarity and how well it can integrate with the other devices they may own.

FAQ: What should I look for in a laptop for writing?

Using word editing software is low intensity. Therefore, choosing a laptop that has a long battery life and large storage space should be seen as a priority.

FAQ: How much should I spend?

A laptop in the range of $500 should cover all of your needs if writing is its primary function. However, you do pay for what you get so going cheaper or more expensive will impact its battery life, processing speeds and storage.

Our Verdict

Editor’s Choice

Google Pixelbook Pro

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Our top Chrome OS recommendation is the impressive Google Pixelbook Go. Plenty of battery life, genuinely fantastic ‘Hush’ keys and top-notch construction make it a top choice for writers.

For those on the lookout for a versatile 2-in-1 laptop, it’s hard to beat the HP Spectre x360 with its beautiful 4K display, excellent specifications, and a comfy keyboard.

In the 15-inch class, the Dell XPS 15 trumps the competition thanks to its stylish construction, superb display, and quality keyboard, although these benefits come at a premium.

Among 13-inch laptops, the Microsoft Surface Laptop 3 is easily the best option for writers with a solid battery, a slim design, an excellent keyboard, all while keeping costs down. Finally, for those on the budget, the ASUS Chromebook Flip is a low-cost, feature-full option.

Google Search Console Updates: More Control Over Data & Email Notifications

Google has updated Search Console with features that give site owners more control over their account preferences.

Site owners now have the option to chose whether performance data for their Search Console property appears in search results.

Also, Google has expanded Search Console’s email preferences with more choice over which types of emails to receive.

Here’s more about each of these updates.

Search Console in Search Results

Google may display data about your Search Console property in search results when entering a query that your site ranks for.

This data is only shown to you when you’re searching while logged into your Google account.

Search Console data in search results is a feature that began popping up in late 2023.

— Jared Gardner (@digagardner) October 3, 2023

What’s shown in the example above is called a summary card.

Previously, site owners could not choose whether or not they saw summary cards in search results

This feature launched unannounced and was rolled out with no option to turn it off.

Now, if site owners would prefer to search Google without their Search Console data staring back at them, they can do that.

You can show or hide summary cards for an individual Search Console property from the settings page.

If you manage multiple sites and do not want to see summary cards for any of them, you can easily disable this for all properties.

Email Preferences

Site owners now have greater control over which types of emails they receive.

In the enhanced email preferences page you can enable or disable emails of specific types, or disable email notifications altogether.

If you unsubscribe from any or all emails you can still see the message in the message panel for your property.

So there’s no need to worry about missing important emails.

But, if you’re inundated with Search Console emails to the point where you need to take swift action, these enhanced preferences should do the job.

Source: Google Search Console Help

15 Best Android Survival Games

Joe Hindy / Android Authority

Survival games are some of the most popular on mobile. It’s also one of the few where the primary mechanic is in the name. You have to survive. Players usually set up a base, craft various things, create defenses from outside threats, find food, and, sometimes, make friends. There are a lot of different takes on the base gameplay mechanics. Most survival games have a distinctive survival feel but manage to remain reasonably separate from one another. Here are the best survival games for Android.

We also have a full list of the best battle royale games. They aren’t hardcore survival games, but do have survival elements. We also recommend Out There: Ω Edition (Google Play). It’s excellent, but it hasn’t been updated in a few years. This War of Mine (Google Play) is also good, despite its crash bug issues.

The best survival games for Android

Price: $14.99

Alien: Isolation is a survival-horror game that takes place in the Alien universe. You play as Amanda Ripley, Ellen Ripley’s daughter, as she goes to a space station to solve the mystery of what happened to her mom. You show up underprepared and underequipped, and thus, the survival experience begins. There are a variety of items and resources to find to help you live through everything as you progress the story. This is an excellent game overall, and a faithful port of the original. The price is a bit high, but there are no in-app purchases to muck things up.

Ark: Survival Evolved

Price: Free to play / $4.99 per month / $39.99 per year

Cataclysm: Dark Days Ahead

Price: Free

Cataclysm: Dark Days Ahead is one of those diamond in the rough survival games. It’s free and open-source with hundreds of contributors. You start out with nothing and try to survive in a randomly generated world. You have monstrosities to defeat, stuff to collect, and a human to protect. The graphics are retro style and the menu system is not great, but if you can get passed that, this is a surprisingly good game. There are also two versions. We linked to the one that is still actively updated. There’s another, older one that isn’t. You can go with either one.

Also try: The best roguelike dungeon crawlers for Android

Crashlands

Price: $4.99

Read next: The best strategy games for Android

Day R Survival

Price: Free to play

Day R Survival is another excellent freemium survival game. This one takes place in the USSR. It includes 2,700 locations on a huge map, tons of items to find, and a relatively decent survival experience. You have to stave off hunger, not get killed by zombies, and avoid radiation where possible. It also boasts an RPG progression system for skills like shelter, mechanics, chemistry, and other such things. It’s a surprisingly deep survival game for its price tag. This one should be fun for a while.

See next: The best Android horror games for a good scare

Don’t Starve: Pocket Edition and Shipwrecked

Price: $4.99 each

Don’t Starve: Pocket Edition is one of the more popular survival games. Your job is to survive and succeed in an unrelenting wilderness. Not doing so will result in your quick and untimely death. Like most games in the genre, you’ll have to collect resources, exploit your environment for your own gains, and do whatever it takes to live. The charm with this one involves the little extras and supernatural elements that set it apart from pure wilderness survival games. Shipwrecked is the second game in the series. It’s just as good as the first one. Most of the mechanics are the same, the only huge difference is the environment. Both games are $4.99 with no in-app purchases.

Doom & Destiny Worlds

Price: $4.99 with in-app purchases

Doom & Destiny Worlds is a light survival game mixed with an RPG. The graphics are SNES-era, so it hits the nostalgia button a bit for older gamers. You start out with a party affected by anmesia. From there, you gather resources, craft items, level up your party, and make your way through the game. The survival elements here are pretty light, but you can drown on your way to an island while exploring so it’s important to manage those types of things. Fans of the first two Doom & Destiny games have quite a few grips about it not playing like the first two games, but as a standalone survival RPG, it’s pretty good. We also recommend checking out classic combat mode, as it’s a little more traditional than the one you start with in-game.

Dysmantle

Price: $9.99

Dysmantle is a survival game that follows the standard survival layout. You start with nothing and must gather resources and build your way into safety. This one is nice because you can break almost everything in the game to use as materials. There are also things to fight or run away from, a crafting system, and various tools to make your life easier. In terms of pure premise, this is a pretty standard survival game. However, the presentation is outstanding, and it’s definitely one of the easier recommendations we can make in this genre. This is one of the good ones, no doubt about it.

Forager

Price: $7.99

Forager is an interesting survival game. It’s a 2D, open-world game where you explore your surroundings, forage for supplies, take down enemies, and build up your base. This has a typical play style. You start with absolutely nothing and eventually wind up with all that you need to survive the game. This one also tosses in some extra elements, like puzzle solving, raid dungeons, and some RPG elements. It’s not much, but it helps keep the game feeling fresh. This one is a premium game at $7.99 with no in-app purchases. Our only gripe is the occasionally laggy and clunky controls.

Read next: The best escape games for Android

Last Day on Earth: Survival

Price: Free to play

LifeAfter

Price: Free to play

LifeAfter is one of the newer survival games on the list. It plays like most post-apocalyptic survival games. A virus sweeps the planet and leaves most people dead or as monsters. You must survive the monster hoard, collect gear, craft stuff, and figure out ways to live. Players can craft safe houses, learn to tend wounds, and forage for food wherever they can. It’s all backed up with some decent graphics and controls. The game’s free-to-play mechanics can be a little stressful at times, but they’re mostly not a big deal.

Minecraft

Price: $6.99 with in-app purchases

Minecraft is one of the world’s most popular survival games on any platform. It’s also great for creative people and other stuff. Survival Mode is the epitome of what survival games should be. For those who don’t know, Minecraft puts you in a randomly generated world, and then you do whatever you want. You can build things, mine things, kill things, grow things, and do pretty much whatever you want. Microsoft made some big moves with Minecraft in 2024 and 2023. You can play this game with people on PC and Xbox One now. It’s one of the few cross-platform games for mobile, PC, and Xbox One.

Terraria (Google Play) is also an outstanding game that utilizes some of the same mechanics, albeit in a different way. You can’t go wrong with either one.

Also check out: The best games like Minecraft on Android

Prospector: Survival Rush

Price: Free to play

Prospector: Survival Rush is an entertaining survival game. It goes for the low-poly approach when it comes to graphics. Otherwise, it’s a life management-style survival game. You find water and resources, build up your base, and try to fend off attacks. The game lets real people raid your base, so there is an element of PvP as well. Some other game features include crafting as well as an in-game economy where you can buy and sell items to other players. You can even lay traps in your base that should alert you when you’re out of the game when people try to raid you. Like most games, it has its pros and cons, but it’s an overall good experience.

Rebel Inc

Price: Free to play

Rebel Inc technically isn’t a survival game but oh my goodness does it feel like one. You start the game as a leader and you try to stabilize a region torn apart by war. You have to rebuild the society, introduce things like clean water, and garner support from the locals. However, you also have to battle corruption, insurgent attacks, and a constant stream of things that need your attention and money. By the end of a round, it feels like a survival game even if its official genre is strategy-simulation. This one is hard and awesome. The game is free and you can get the pro version for $3.99. There are also in-game cheats and extras you can purchase with additional money as well. All things you purchase are permanent content and not consumable power-ups. This one is also really hard once you get to higher levels. Good luck!

The Wanderer

Price: Free to play

The Wanderer is one of the most interesting survival games on mobile. It doesn’t make heavy use of complicated controls or graphics. Instead, you make decisions that impact how the game goes, and mostly watch how it plays out. You can do things like take in a wild dog to help you find supplies, but it needs food and water like you do, so there is a give and take. The game ramps up in difficulty as you play, but does so in a natural and good-feeling way. There are a ton of different things you can do in the game, down to picking locks and taking on quests to kill bandits. It’s quite good. Definitely a sleeper pick on this list.

An Introduction To Graph Theory And Network Analysis (With Python Codes)

Introduction

“A picture speaks a thousand words” is one of the most commonly used phrases. But a graph speaks so much more than that. A visual representation of data, in the form of graphs, helps us gain actionable insights and make better data driven decisions based on them.

But to truly understand what graphs are and why they are used, we will need to understand a concept known as Graph Theory. Understanding this concept makes us better programmers (and better data science professionals!).

But if you have tried to understand this concept before, you’ll have come across tons of formulae and dry theoretical concepts. That is why we decided to write this blog post. We have explained the concepts and then provided illustrations so you can follow along and intuitively understand how the functions are performing. This is a detailed post, because we believe that providing a proper explanation of this concept is a much preferred option over succinct definitions.

In this article, we will look at what graphs are, their applications and a bit of history about them. We’ll also cover some Graph Theory concepts and then take up a case study using python to cement our understanding.

Ready? Let’s dive into it.

Table of Contents

Graphs and their applications

History and why graphs?

Terminologies you need to know

Graph Theory Concepts

Getting familiar with Graphs in python

Analysis on a dataset

Graphs and their applications

Let us look at a simple graph to understand the concept. Look at the image below –

Consider that this graph represents the places in a city that people generally visit, and the path that was followed by a visitor of that city. Let us consider V as the places and E as the path to travel from one place to another.

V = {v1, v2, v3, v4, v5} E = {(v1,v2), (v2,v5), (v5, v5), (v4,v5), (v4,v4)}

The edge (u,v) is the same as the edge (v,u) – They are unordered pairs.

Concretely – Graphs are mathematical structures used to study pairwise relationships between objects and entities. It is a branch of Discrete Mathematics and has found multiple applications in Computer Science, Chemistry, Linguistics, Operations Research, Sociology etc.

The Data Science and Analytics field has also used Graphs to model various structures and problems. As a Data Scientist, you should be able to solve problems in an efficient manner and Graphs provide a mechanism to do that in cases where the data is arranged in a specific way.

Formally,

A Graph is a pair of sets. G = (V,E). V is the set of vertices. E is a set of edges. E is made up of pairs of elements from V (unordered pair)

A DiGraph is also a pair of sets. D = (V,A). V is the set of vertices. A is the set of arcs. A is made up of pairs of elements from V (ordered pair)

In the case of digraphs, there is a distinction between `(u,v)` and `(v,u)`. Usually the edges are called arcs in such cases to indicate a notion of direction.

There are packages that exist in R and Python to analyze data using Graph theory concepts. In this article we will be briefly looking at some of the concepts and analyze a dataset using Networkx Python package.

from IPython.display import Image Image('images/network.PNG')

Image('images/usecase.PNG')

From the above examples it is clear that the applications of Graphs in Data Analytics are numerous and vast. Let us look at a few use cases:

Marketing Analytics – Graphs can be used to figure out the most influential people in a Social Network. Advertisers and Marketers can estimate the biggest bang for the marketing buck by routing their message through the most influential people in a Social Network

Banking Transactions – Graphs can be used to find unusual patterns helping in mitigating Fraudulent transactions. There have been examples where Terrorist activity has been detected by analyzing the flow of money across interconnected Banking networks

Supply Chain – Graphs help in identifying optimum routes for your delivery trucks and in identifying locations for warehouses and delivery centres

Pharma – Pharma companies can optimize the routes of the salesman using Graph theory. This helps in cutting costs and reducing the travel time for salesman

Telecom – Telecom companies typically use Graphs (Voronoi diagrams) to understand the quantity and location of Cell towers to ensure maximum coverage

History and Why Graphs? History of Graphs

If you want to know more on how the ideas from graph has been formlated – read on!

The origin of the theory can be traced back to the Konigsberg bridge problem (circa 1730s). The problem asks if the seven bridges in the city of Konigsberg can be traversed under the following constraints

no doubling back

you end at the same place you started

This is the same as asking if the multigraph of 4 nodes and 7 edges has an Eulerian cycle (An Eulerian cycle is an Eulerian path that starts and ends on the same Vertex. And an Eulerian path is a path in a Graph that traverses each edge exactly once. More Terminology is given below). This problem led to the concept of Eulerian Graph. In the case of the Konigsberg bridge problem the answer is no and it was first answered by (you guessed it) Euler.

In 1840, A.F Mobius gave the idea of complete graph and bipartite graph and Kuratowski proved that they are planar by means of recreational problems. The concept of tree, (a connected graph without cycles) was implemented by Gustav Kirchhoff in 1845, and he employed graph theoretical ideas in the calculation of currents in electrical networks or circuits.

In 1852, Thomas Gutherie found the famous four color problem. Then in 1856, Thomas. P. Kirkman and William R.Hamilton studied cycles on polyhydra and invented the concept called Hamiltonian graph by studying trips that visited certain sites exactly once. In 1913, H.Dudeney mentioned a puzzle problem. Eventhough the four color problem was invented it was solved only after a century by Kenneth Appel and Wolfgang Haken. This time is considered as the birth of Graph Theory.

Caley studied particular analytical forms from differential calculus to study the trees. This had many implications in theoretical chemistry. This lead to the invention of enumerative graph theory. Any how the term “Graph” was introduced by Sylvester in 1878 where he drew an analogy between “Quantic invariants” and covariants of algebra and molecular diagrams.

In 1941, Ramsey worked on colorations which lead to the identification of another branch of graph theory called extremel graph theory. In 1969, the four color problem was solved using computers by Heinrich. The study of asymptotic graph connectivity gave rise to random graph theory. The histories of Graph Theory and Topology are also closely related. They share many common concepts and theorems.

Image('images/Konigsberg.PNG', width = 800)

Why Graphs?

Here are a few points that help you motivate to use graphs in your day-to-day data science problems –

Graphs provide a better way of dealing with abstract concepts like relationships and interactions. They also offer an intuitively visual way of thinking about these concepts. Graphs also form a natural basis for analyzing relationships in a Social context

Graph Databases have become common computational tools and alternatives to SQL and NoSQL databases

Graphs are used to model analytics workflows in the form of DAGs (Directed acyclic graphs)

Some Neural Network Frameworks also use DAGs to model the various operations in different layers

Graph Theory concepts are used to study and model Social Networks, Fraud patterns, Power consumption patterns, Virality and Influence in Social Media. Social Network Analysis (SNA) is probably the best known application of Graph Theory for Data Science

It is used in Clustering algorithms – Specifically K-Means

System Dynamics also uses some Graph Theory concepts – Specifically loops

Path Optimization is a subset of the Optimization problem that also uses Graph concepts

From a Computer Science perspective – Graphs offer computational efficiency. The Big O complexity for some algorithms is better for data arranged in the form of Graphs (compared to tabular data)

Terminology you should know

Before you go any further into the article, it is recommended that you should get familiar with these terminologies.

The vertices u and v are called the end vertices of the edge (u,v)

If two edges have the same end vertices they are Parallel

An edge of the form (v,v) is a loop

A Graph is simple if it has no parallel edges and loops

A Graph is said to be Empty if it has no edges. Meaning E is empty

A Graph is a Null Graph if it has no vertices. Meaning V and E is empty

A Graph with only 1 Vertex is a Trivial graph

Edges are Adjacent if they have a common vertex. Vertices are Adjacent if they have a common edge

The degree of the vertex v, written as d(v), is the number of edges with v as an end vertex. By convention, we count a loop twice and parallel edges contribute separately

Isolated Vertices are vertices with degree 1. d(1) vertices are isolated

A Graph is Complete if its edge set contains every possible edge between ALL of the vertices

A Walk in a Graph G = (V,E) is a finite, alternating sequence of the form 

V

i

E

i

ViEi

 consisting of vertices and edges of the graph G

A Walk is Open if the initial and final vertices are different. A Walk is Closed if the initial and final vertices are the same

A Walk is a Trail if ANY edge is traversed atmost once

A Trail is a Path if ANY vertex is traversed atmost once (Except for a closed walk)

A Closed Path is a Circuit – Analogous to electrical circuits

Graph Theory concepts

In this section, we’ll look at some of the concepts useful for Data Analysis (in no particular order). Please note that there are a lot more concepts that require a depth which is out of scope of this article. So let’s get into it.

Average Path Length

The average of the shortest path lengths for all possible node pairs. Gives a measure of ‘tightness’ of the Graph and can be used to understand how quickly/easily something flows in this Network.

BFS and DFS

Breadth first search and Depth first search are two different algorithms used to search for Nodes in a Graph. They are typically used to figure out if we can reach a Node from a given Node. This is also known as Graph Traversal

The aim of the BFS is to traverse the Graph as close as possible to the root Node, while the DFS algorithm aims to move as far as possible away from the root node.

Centrality

One of the most widely used and important conceptual tools for analysing networks. Centrality aims to find the most important nodes in a network. There may be different notions of “important” and hence there are many centrality measures. Centrality measures themselves have a form of classification (or Types of centrality measures). There are measures that are characterized by flow along the edges and those that are characterized by Walk Structure.

Some of the most commonly used ones are:

Degree Centrality – The first and conceptually the simplest Centrality definition. This is the number of edges connected to a node. In the case of a directed graph, we can have 2 degree centrality measures. Inflow and Outflow Centrality

Closeness Centrality – Of a node is the average length of the shortest path from the node to all other nodes

Betweenness Centrality – Number of times a node is present in the shortest path between 2 other nodes

These centrality measures have variants and the definitions can be implemented using various algorithms. All in all, this means a large number of definitions and algorithms.

Network Density

A measure of how many edges a Graph has. The actual definition will vary depending on type of Graph and the context in which the question is asked. For a complete undirected Graph the Density is 1, while it is 0 for an empty Graph. Graph Density can be greater than 1 in some situations (involving loops).

Graph Randomizations

While the definitions of some Graph metrics maybe easy to calculate, it is not easy to understand their relative importance. We use Network/Graph Randomizations in such cases. We calculate the metric for the Graph at hand and for another similar Graph that is randomly generated. This similarity can for example be the same number of density and nodes. Typically we generate a 1000 similar random graphs and calculate the Graph metric for each of them and then compare it with the same metric for the Graph at hand to arrive at some notion of a benchmark.

In Data Science when trying to make a claim about a Graph it helps if it is contrasted with some randomly generated Graphs.

Getting Familiar with Graphs in python

We will be using the networkx package in Python. It can be installed in the Root environment of Anaconda (if you are using the Anaconda distribution of Python). You can also pip install it.

Let us look at some common things that can be done with the Networkx package. These include importing and creating a Graph and ways to visualize it.

Graph Creation import networkx as nx # Creating a Graph G = nx.Graph() # Right now G is empty # Add a node G.add_node(1) G.add_nodes_from([2,3]) # You can also add a list of nodes by passing a list argument # Add edges G.add_edge(1,2) e = (2,3) G.add_edge(*e) # * unpacks the tuple G.add_edges_from([(1,2), (1,3)]) # Just like nodes we can add edges from a list

Node and Edge attributes can be added along with the creation of Nodes and Edges by passing a tuple containing node and attribute dict.

In addition to constructing graphs node-by-node or edge-by-edge, they can also be generated by applying classic graph operations, such as:

subgraph(G, nbunch) - induced subgraph view of G on nodes in nbunch union(G1,G2) - graph union disjoint_union(G1,G2) - graph union assuming all nodes are different cartesian_product(G1,G2) - return Cartesian product graph compose(G1,G2) - combine graphs identifying nodes common to both complement(G) - graph complement create_empty_copy(G) - return an empty copy of the same graph class convert_to_undirected(G) - return an undirected representation of G convert_to_directed(G) - return a directed representation of G

Separate classes exist for different types of Graphs. For example the nx.DiGraph() class allows you to create a Directed Graph. Specific graphs containing paths can be created directly using a single method. For a full list of Graph creation methods please refer to the full documentation. Link is given at the end of the article.

Image('images/graphclasses.PNG', width = 400)

Accessing edges and nodes

Nodes and Edges can be accessed together using the G.nodes() and G.edges() methods. Individual nodes and edges can be accessed using the bracket/subscript notation.

G.nodes()

NodeView((1, 2, 3))

G.edges()

EdgeView([(1, 2), (1, 3), (2, 3)])

G[1] # same as G.adj[1]

AtlasView({2: {}, 3: {}})

G[1][2]

{}

G.edges[1, 2]

{}



Graph Visualization

Networkx provides basic functionality for visualizing graphs, but its main goal is to enable graph analysis rather than perform graph visualization. Graph visualization is hard and we will have to use specific tools dedicated for this task. Matplotlib offers some convenience functions. But GraphViz is probably the best tool for us as it offers a Python interface in the form of PyGraphViz (link to documentation below).

%matplotlib inline import matplotlib.pyplot as plt nx.draw(G)

import pygraphviz as pgv d={'1': {'2': None}, '2': {'1': None, '3': None}, '3': {'1': None}} A = pgv.AGraph(data=d) print(A) # This is the 'string' or simple representation of the Graph Output: strict graph "" { 1 -- 2; 2 -- 3; 3 -- 1; }

PyGraphviz provides great control over the individual attributes of the edges and nodes. We can get very beautiful visualizations using it.

# Let us create another Graph where we can individually control the colour of each node B = pgv.AGraph() # Setting node attributes that are common for all nodes B.node_attr['style']='filled' B.node_attr['shape']='circle' B.node_attr['fixedsize']='true' B.node_attr['fontcolor']='#FFFFFF' # Creating and setting node attributes that vary for each node (using a for loop) for i in range(16): B.add_edge(0,i) n=B.get_node(i) n.attr['fillcolor']="#%2x0000"%(i*16) n.attr['height']="%s"%(i/16.0+0.5) n.attr['width']="%s"%(i/16.0+0.5) B.draw('star.png',prog="circo") # This creates a .png file in the local directory. Displayed below. Image('images/star.png', width=650) # The Graph visualization we created above.

Usually, visualization is thought of as a separate task from Graph analysis. A graph once analyzed is exported as a Dotfile. This Dotfile is then visualized separately to illustrate a specific point we are trying to make.

Analysis on a Dataset

We will be looking to take a generic dataset (not one that is specifically intended to be used for Graphs) and do some manipulation (in pandas) so that it can be ingested into a Graph in the form of a edgelist. And edgelist is a list of tuples that contain the vertices defining every edge

The dataset we will be looking at comes from the Airlines Industry. It has some basic information on the Airline routes. There is a Source of a journey and a destination. There are also a few columns indicating arrival and departure times for each journey. As you can imagine this dataset lends itself beautifully to be analysed as a Graph. Imagine a few cities (nodes) connected by airline routes (edges). If you are an airline carrier, you can then proceed to ask a few questions like

What is the shortest way to get from A to B? In terms of distance and in terms of time

Is there a way to go from C to D?

Which airports have the heaviest traffic?

Which airport in “in between” most other airports? So that it can be converted into a local hub

import pandas as pd import numpy as np data = pd.read_csv('data/Airlines.csv') data.shape (100, 16) data.dtypes year int64 month int64 day int64 dep_time float64 sched_dep_time int64 dep_delay float64 arr_time float64 sched_arr_time int64 arr_delay float64 carrier object flight int64 tailnum object origin object dest object air_time float64 distance int64 dtype: object

We notice that origin and destination look like good choices for Nodes. Everything can then be imagined as either node or edge attributes. A single edge can be thought of as a journey. And such a journey will have various times, a flight number, an airplane tail number etc associated with it

We notice that the year, month, day and time information is spread over many columns. We want to create one datetime column containing all of this information. We also need to keep scheduled and actual time of arrival and departure separate. So we should finally have 4 datetime columns (Scheduled and actual times of arrival and departure)

Additionally, the time columns are not in a proper format. 4:30 pm is represented as 1630 instead of 16:30. There is no delimiter to split that column. One approach is to use pandas string methods and regular expressions

We should also note that sched_dep_time and sched_arr_time are int64 dtype and dep_time and arr_time are float64 dtype

An additional complication is NaN values

# converting sched_dep_time to 'std' - Scheduled time of departure data['std'] = data.sched_dep_time.astype(str).str.replace('(d{2}$)', '') + ':' + data.sched_dep_time.astype(str).str.extract('(d{2}$)', expand=False) + ':00' # converting sched_arr_time to 'sta' - Scheduled time of arrival data['sta'] = data.sched_arr_time.astype(str).str.replace('(d{2}$)', '') + ':' + data.sched_arr_time.astype(str).str.extract('(d{2}$)', expand=False) + ':00' # converting dep_time to 'atd' - Actual time of departure data['atd'] = data.dep_time.fillna(0).astype(np.int64).astype(str).str.replace('(d{2}$)', '') + ':' + data.dep_time.fillna(0).astype(np.int64).astype(str).str.extract('(d{2}$)', expand=False) + ':00' # converting arr_time to 'ata' - Actual time of arrival data['ata'] = data.arr_time.fillna(0).astype(np.int64).astype(str).str.replace('(d{2}$)', '') + ':' + data.arr_time.fillna(0).astype(np.int64).astype(str).str.extract('(d{2}$)', expand=False) + ':00'

We now have time columns in the format we wanted. Finally we may want to combine the year, month and day columns into a date column. This is not an absolutely necessary step. But we can easily obtain the year, month and day (and other) information once it is converted into datetime format.

data['date'] = pd.to_datetime(data[['year', 'month', 'day']]) # finally we drop the columns we don't need data = data.drop(columns = ['year', 'month', 'day'])

Now import the dataset using the networkx function that ingests a pandas dataframe directly. Just like Graph creation there are multiple ways Data can be ingested into a Graph from multiple formats.

import networkx as nx FG = nx.from_pandas_edgelist(data, source='origin', target='dest', edge_attr=True,) FG.nodes()

Output:

NodeView(('EWR', 'MEM', 'LGA', 'FLL', 'SEA', 'JFK', 'DEN', 'ORD', 'MIA', 'PBI', 'MCO', 'CMH', 'MSP', 'IAD', 'CLT', 'TPA', 'DCA', 'SJU', 'ATL', 'BHM', 'SRQ', 'MSY', 'DTW', 'LAX', 'JAX', 'RDU', 'MDW', 'DFW', 'IAH', 'SFO', 'STL', 'CVG', 'IND', 'RSW', 'BOS', 'CLE')) FG.edges()

Output:

EdgeView([('EWR', 'MEM'), ('EWR', 'SEA'), ('EWR', 'MIA'), ('EWR', 'ORD'), ('EWR', 'MSP'), ('EWR', 'TPA'), ('EWR', 'MSY'), ('EWR', 'DFW'), ('EWR', 'IAH'), ('EWR', 'SFO'), ('EWR', 'CVG'), ('EWR', 'IND'), ('EWR', 'RDU'), ('EWR', 'IAD'), ('EWR', 'RSW'), ('EWR', 'BOS'), ('EWR', 'PBI'), ('EWR', 'LAX'), ('EWR', 'MCO'), ('EWR', 'SJU'), ('LGA', 'FLL'), ('LGA', 'ORD'), ('LGA', 'PBI'), ('LGA', 'CMH'), ('LGA', 'IAD'), ('LGA', 'CLT'), ('LGA', 'MIA'), ('LGA', 'DCA'), ('LGA', 'BHM'), ('LGA', 'RDU'), ('LGA', 'ATL'), ('LGA', 'TPA'), ('LGA', 'MDW'), ('LGA', 'DEN'), ('LGA', 'MSP'), ('LGA', 'DTW'), ('LGA', 'STL'), ('LGA', 'MCO'), ('LGA', 'CVG'), ('LGA', 'IAH'), ('FLL', 'JFK'), ('SEA', 'JFK'), ('JFK', 'DEN'), ('JFK', 'MCO'), ('JFK', 'TPA'), ('JFK', 'SJU'), ('JFK', 'ATL'), ('JFK', 'SRQ'), ('JFK', 'DCA'), ('JFK', 'DTW'), ('JFK', 'LAX'), ('JFK', 'JAX'), ('JFK', 'CLT'), ('JFK', 'PBI'), ('JFK', 'CLE'), ('JFK', 'IAD'), ('JFK', 'BOS')]) nx.draw_networkx(FG, with_labels=True) # Quick view of the Graph. As expected we see 3 very busy airports

nx.algorithms.degree_centrality(FG) # Notice the 3 airports from which all of our 100 rows of data originates nx.density(FG) # Average edge density of the Graphs

Output:

0.09047619047619047 nx.average_shortest_path_length(FG) # Average shortest path length for ALL paths in the Graph

Output:

2.36984126984127 nx.average_degree_connectivity(FG) # For a node of degree k - What is the average of its neighbours' degree?

Output:

{1: 19.307692307692307, 2: 19.0625, 3: 19.0, 17: 2.0588235294117645, 20: 1.95}

As is obvious from looking at the Graph visualization (way above) – There are multiple paths from some airports to others. Let us say we want to calculate the shortest possible route between 2 such airports. Right off the bat we can think of a couple of ways of doing it

There is the shortest path by distance

There is the shortest path by flight time

What we can do is to calculate the shortest path algorithm by weighing the paths with either the distance or airtime. Please note that this is an approximate solution – The actual problem to solve is to calculate the shortest path factoring in the availability of a flight when you reach your transfer airport + wait time for the transfer. This is a more complete approach and this is how humans normally plan their travel. For the purposes of this article we will just assume that is flight is readily available when you reach an airport and calculate the shortest path using the airtime as the weight

Let us take the example of JAX and DFW airports:

# Let us find all the paths available for path in nx.all_simple_paths(FG, source='JAX', target='DFW'): print(path) # Let us find the dijkstra path from JAX to DFW. dijpath = nx.dijkstra_path(FG, source='JAX', target='DFW') dijpath

Output:

['JAX', 'JFK', 'SEA', 'EWR', 'DFW'] # Let us try to find the dijkstra path weighted by airtime (approximate case) shortpath = nx.dijkstra_path(FG, source='JAX', target='DFW', weight='air_time') shortpath

Output:

['JAX', 'JFK', 'BOS', 'EWR', 'DFW'] Conclusion

This article has at best only managed a superficial introduction to the very interesting field of Graph Theory and Network analysis. Knowledge of the theory and the Python packages will add a valuable toolset to any Data Scientist’s arsenal. For the dataset used above, a series of other questions can be asked like:

Find the shortest path between two airports given Cost, Airtime and Availability?

You are an airline carrier and you have a fleet of airplanes. You have an idea of the demand available for your flights. Given that you have permission to operate 2 more airplanes (or add 2 airplanes to your fleet) which routes will you operate them on to maximize profitability?

Can you rearrange the flights and schedules to optimize a certain parameter (like Timeliness or Profitability etc)

Bibiliography and References About the Author

Srivatsa currently works for TheMathCompany and has over 7.5 years of experience in Decision Sciences and Analytics. He has grown, led & scaled global teams across functions, industries & geographies. He has led India Delivery for a cross industry portfolio totalling $10M in revenues. He has also conducted several client workshops and training sessions to help level up technical and business domain knowledge.

During his career span, he has led premium client engagements with Industry leaders in Technology, e-commerce and retail. He helped set up the Analytics Center of Excellence for one of the world’s largest Insurance companies.

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High Ping Spikes And Lag In Lost Ark: 5 Ways To Fix It

High Ping Spikes and Lag in Lost Ark: 5 Ways to Fix It Our easy solutions will surely help fix high ping in Lost Ark

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