Video Game Industry modeled by complex networks

1. Presented By Tony Morelli Video Game Industry modeled by complex networks

2. Outline • Intro/Problem description • Visual Network Representations • Numerical Network Representations • Questions/Comments

3. INTRO • Has the organization of the video game industry changed in the last 20 years? • Consoles • Game Titles • Producers • Developers

4. Consoles to Analyze • Group A – Classic Consoles • Atari 2600 (1977)

5. Consoles to Analyze • Group A – Classic Consoles • Atari 2600 (1977) • Nintendo Entertainment System (1983)

6. Consoles to Analyze • Group A – Classic Consoles • Atari 2600 (1977) • Nintendo Entertainment System (1983) • Sega Master System (1985)

7. Consoles to Analyze • Group B – Current Consoles • XBOX 360 (2005)

8. Consoles to Analyze • Group B – Current Consoles • XBOX 360 (2005) • Playstation 3 (2006)

9. Consoles to Analyze • Group B – Current Consoles • XBOX 360 (2005) • Playstation 3 (2006) • Nintendo Wii (2006)

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

11. Background/Related Work • Comparison has not previously been done • Need to investigate techniques for network comparison

12. Methods of Comparison • Graphical • Numeric

13. Soft Drink Industry

14. Soft Drink Industry

15. Seed Industry Consolidation

16. Seed Industry Consolidation http://www.youtube.com/watch?v=nBBXLZWyXBQ

17. Non-Scientific Network

18. Graphical Representation • Use Colors and Sizes • Use Pajek to Generate • Use morphing animation to show changes from classic vs current

19. Graphical Difference • How do the two graphs differ visually? • Hypothesis – Current consoles have less producers with more content than classic.

20. Numerical Analysis • Several Studies have been done showing numerical analysis of networks • Important to find metrics and comparison methods

21. Network Topologies, Power Laws, and Hierarchy • Published June 2001 • Analyzes Topology Generators

22. Network Topologies, Power Laws, and Hierarchy • Internet researches had used • GT-ITM • Tiers • Generated a simulated internet to test and analyze

23. 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

24. 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

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

26. 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

27. 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

28. 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

29. 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.

30. Comparison of Translations • How accurate are software based translators? • Portuguese->Spanish • Portuguese->English

31. Comparison of Translations • Translators compared • Human translated • Free Translation • Intertran

32. 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

33. 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

34. Comparison of Translations • Results • Closer the In-Degree - More accurate translation • Closer the Out-Degree - More accurate translation

35. Comparison of Translations • Results • Avg Pearson Coefficient Avg Angular Coefficient

36. Comparison of Translations • Results

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

38. 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 • 26-172 nodes in each web

39. 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

40. 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

41. 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

42. Finding the Most Prominent Group in Complex Networks • The study evaluates quick methods of finding the most prominent group

43. Finding the Most Prominent Group in Complex Networks • 2 algorithms • Heuristic Search • Greedy Choice

44. Finding the Most Prominent Group in Complex Networks • 2 algorithms (Lots of math) • Heuristic Search • Fastest • Greedy Choice • Most accurate

45. Finding the Most Prominent Group in Complex Networks • Useful to this project? • Video game network is probably too small to benefit from either method

46. Statistical Methods of Complex Networks • Average Path Length • Clustering Coefficient • Degree Distribution • Spectral Properties • Directly related to the graph’s topological features

47. 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

48. 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

49. Apply to video games • Numerical • Clustering Coefficient • Average out degree • Expansion at each level • All will be normalized by number of titles

50. Work so far… • Scraper has been written • Written in C# • Crawled the websites to gather console, publisher, developer, title for all six consoles