1. Presented By Tony Morelli

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.games-db.com  www.ign.com (Current Consoles) www.ign.com  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

15. Seed Industry Consolidation

16. 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 ODIDCCODIDCC Human0.930.940.440.991.00.44 Apertium0.970.980.931.011.030.85 Intertran0.760.800.580.981.481.01  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

51. Questions/Comments?

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