VIDEO GAME INDUSTRY

MODELED BY COMPLEX

NETWORKS

Presented By Tony Morelli

Outline Intro/Problem description Visual Network Representations Numerical Network Representations Questions/Comments

INTRO Has the organization of the video game

industry changed in the last 20 years?ConsolesGame TitlesProducersDevelopers

Consoles to Analyze Group A – Classic Consoles

Atari 2600 (1977)

Consoles to Analyze Group A – Classic Consoles

Atari 2600 (1977)Nintendo Entertainment System (1983)

Consoles to Analyze Group A – Classic Consoles

Atari 2600 (1977)Nintendo Entertainment System (1983)Sega Master System (1985)

Consoles to Analyze Group B – Current Consoles

XBOX 360 (2005)

Consoles to Analyze Group B – Current Consoles

XBOX 360 (2005)Playstation 3 (2006)

Consoles to Analyze Group B – Current Consoles

XBOX 360 (2005)Playstation 3 (2006)Nintendo Wii (2006)

Where is the data? www.games-db.com (Classic Consoles) www.ign.com (Current Consoles) Console,Title,Developer,Publisher

Background/Related Work Comparison has not previously been

done Need to investigate techniques for

network comparison

Methods of Comparison Graphical Numeric

Soft Drink Industry

Soft Drink Industry

Seed Industry Consolidation

Seed Industry Consolidation

http://www.youtube.com/watch?v=nBBXLZWyXBQ

Non-Scientific Network

Graphical Representation Use Colors and Sizes Use Pajek to Generate Use morphing animation to show

changes from classic vs current

Graphical Difference How do the two graphs differ visually?

Hypothesis – Current consoles have less producers with more content than classic.

Numerical Analysis Several Studies have been done

showing numerical analysis of networks Important to find metrics and

comparison methods

Network Topologies, Power Laws, and Hierarchy

Published June 2001 Analyzes Topology Generators

Network Topologies, Power Laws, and Hierarchy

Internet researches had usedGT-ITMTiers

Generated a simulated internet to test and analyze

Network Topologies, Power Laws, and Hierarchy

Faloutsos found:Internet’s degree distribution is power lawGenerated topologies are notTherefore generated topologies are a poor

choice to run studies on

Network Topologies, Power Laws, and Hierarchy

This paper focusses on a comparison ofDegree-based generators

○ Degree Distribution is the focusStructural generators

○ A hierarchical structure is the focus

Network Topologies, Power Laws, and Hierarchy

Found Degree Based Generators are betterBased on the metrics they usedWhat are these metrics?

Network Topologies, Power Laws, and Hierarchy

MetricsExpansion

○ “The average fraction of nodes in the graph that fall within a ball of radius r, centered at a node in the topology

Network Topologies, Power Laws, and Hierarchy

MetricsResilience

○ How tolerant is the network to failures?○ Cut a single link in a tree

No longer connected○ Cut a single link in a random graph

Probably OK○ Average cut-set size within an N node ball

around any node in the topology

Network Topologies, Power Laws, and Hierarchy

MetricsDistortion

○ Take a random node and all nodes connected to it within n hops

○ Create a spanning tree on this subgraph○ The average distance between vertices that

are connected in the original subgraph is the distortion

Network Topologies, Power Laws, and Hierarchy What metrics will I use from this paper?

Expansion seems goodDistortion and Resilience probably will not

be used.

Comparison of Translations How accurate are software based

translators? Portuguese->Spanish Portuguese->English

Comparison of Translations Translators compared

Human translatedFree TranslationIntertran

Comparison of Translations Methods

Model translated text as a directed graphNodes connected together based on

sequence of appearance in translationThe 2 machine translated networks

compared to the human translated network

Comparison of Translations Metrics

In-degree○ Frequency a word was the second word

Out-degree○ Frequency a word was the first word

Clustering Coefficient○ How much does the graph cluster together

Comparison of Translations Results

Closer the In-Degree - More accurate translation

Closer the Out-Degree - More accurate translation

Comparison of Translations

OD ID CC OD ID CCHuman 0.93 0.94 0.44 0.99 1.0 0.44

Apertium 0.97 0.98 0.93 1.01 1.03 0.85

Intertran 0.76 0.80 0.58 0.98 1.48 1.01

ResultsAvg Pearson Coefficient Avg Angular Coefficient

Comparison of Translations Results

Comparison of Translations Which metrics to use?

In-degree – Not relevantOut-degree – Could be usefulClustering Coefficient - Useful

Food-web structure and network theory

Are food web networks small world or scale free?

Food WebsRelationships in ecosystems

○ Who eats who16 food webs26-172 nodes in each web

Food-web structure and network theory

MetricsAverage shortest path length between all pairs

of speciesClustering CoefficientAverage fraction of pairs of species one link

away from a species that are also linked to each other

Cumulative degree distributionConnectance

○ The fraction of all possible links that are realized in a network

Food-web structure and network theory

ResultsSome characteristics met the standards for

small world and scale freeClustering was low

○ Could be because of network size

Finding the Most Prominent Group in Complex Networks Group Betweenness Centrality

Used to evaluate the prominence of a group of vertices

Might be time consuming to evaluate

Finding the Most Prominent Group in Complex Networks The study evaluates quick methods of

finding the most prominent group

Finding the Most Prominent Group in Complex Networks 2 algorithms

Heuristic SearchGreedy Choice

Finding the Most Prominent Group in Complex Networks 2 algorithms (Lots of math)

Heuristic Search○ Fastest

Greedy Choice○ Most accurate

Finding the Most Prominent Group in Complex Networks Useful to this project?

Video game network is probably too small to benefit from either method

Statistical Methods of Complex Networks

Average Path Length Clustering Coefficient Degree Distribution Spectral Properties

Directly related to the graph’s topological features

Statistical Methods of Complex Networks

Metrics UsedAverage Path Length

○ Not very useful for Video Game networkClustering Coefficient

○ Will useDegree Distribution

○ Will useSpectral Properties

○ Topology is already known – not useful

Apply to video games Graphical

Size and color○ Larger node has more titles tied to it○ Colorize publishers to easily distinguish○ Create an animation of classic to current

Apply to video games Numerical

Clustering CoefficientAverage out degree Expansion at each level

All will be normalized by number of titles

Work so far… Scraper has been written Written in C# Crawled the websites to gather console,

publisher, developer, title for all six consoles

Questions/Comments?

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