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? Consoles Game Titles Producers Developers

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?  (Classic Consoles)  (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

Seed Industry Consolidation

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 used GT-ITM Tiers  Generated a simulated internet to test and analyze

Network Topologies, Power Laws, and Hierarchy  Faloutsos found: Internet’s degree distribution is power law Generated topologies are not Therefore generated topologies are a poor choice to run studies on

Network Topologies, Power Laws, and Hierarchy  This paper focusses on a comparison of Degree-based generators ○ Degree Distribution is the focus Structural generators ○ A hierarchical structure is the focus

Network Topologies, Power Laws, and Hierarchy  Found Degree Based Generators are better Based on the metrics they used What are these metrics?

Network Topologies, Power Laws, and Hierarchy  Metrics Expansion ○ “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  Metrics Resilience ○ 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  Metrics Distortion ○ 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 good Distortion 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 translated Free Translation Intertran

Comparison of Translations  Methods Model translated text as a directed graph Nodes connected together based on sequence of appearance in translation The 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 ODIDCCODIDCC Human Apertium Intertran  Results Avg Pearson Coefficient Avg Angular Coefficient

Comparison of Translations  Results

Comparison of Translations  Which metrics to use? In-degree – Not relevant Out-degree – Could be useful Clustering Coefficient - Useful

Food-web structure and network theory  Are food web networks small world or scale free?  Food Webs Relationships in ecosystems ○ Who eats who 16 food webs nodes in each web

Food-web structure and network theory  Metrics Average shortest path length between all pairs of species Clustering Coefficient Average fraction of pairs of species one link away from a species that are also linked to each other Cumulative degree distribution Connectance ○ The fraction of all possible links that are realized in a network

Food-web structure and network theory  Results Some characteristics met the standards for small world and scale free Clustering 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 Search Greedy 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 Used Average Path Length ○ Not very useful for Video Game network Clustering Coefficient ○ Will use Degree Distribution ○ Will use Spectral 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 Coefficient Average 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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