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DivRank: Interplay of Prestige and Diversity in Information Networks

DivRank: Interplay of Prestige and Diversity in Information Networks. Qiaozhu Mei 1,2 , Jian Guo 3 , Dragomir Radev 1,2 1. School of Information 2. Computer Science and Engineering 3. Department of Statistics University of Michigan. Diversity in Ranking.

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DivRank: Interplay of Prestige and Diversity in Information Networks

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  1. DivRank: Interplay of Prestige and Diversity in Information Networks Qiaozhu Mei1,2, Jian Guo3, Dragomir Radev1,2 1.School of Information 2.Computer Science and Engineering 3. Department of Statistics University of Michigan

  2. Diversity in Ranking Ranking papers, people, web pages, movies, restaurants… Web search; ads; recommender systems … Network based ranking – centrality/prestige

  3. Ranking by Random Walks b d a c ? Ranking using stationary distribution E.g., PageRank

  4. Reinforcements in Random Walks Conformity! Source - http://www.resettlementagency.co.uk/modern-world-migration/ Random walks are not random - rich gets richer; • e.g., civilization/immigration – big cities attract larger population; • Tourism – busy restaurants attract more visitors;

  5. Vertex-Reinforced Random Walk (Pemantle 92) b d a transition probabilities change over time c Reinforced random walk: transition probability is reinforced by the weight (number of visits) of the target state

  6. DivRank b a “organic” transition probability Random jump, could be personalized c Cumulative DivRank: Pointwise DivRank: A smoothed version of Vertex-reinforced Random Walk Adding self-links; Efficient approximations: use to approximate

  7. Experiments Three applications • Ranking movie actors (in co-star network) • Ranking authors/papers (in author/paper-citation network) • Text summarization (ranking sentences) Evaluation metrics: • diversity: density of subgraph; country coverage (actors) • quality: h-index (authors); # citation (papers); • quality + diversity: movie coverage (actors); impact coverage (papers); ROUGE (text summarization)

  8. Results Paper citation: Pagerank Grasshopper Impact coverage Density Divrank Text Summarization: Divrank >> Grasshopper/MMR >> Pagerank

  9. Why Does it Work? b c b a Stay here or go to neighbors? Rich gets richer • Related to Polya’s urn and preferential attachment Compete for resource in neighborhood • Prestigious node absorbs weights of its neighbors An optimization explanation

  10. Summary DivRank – Prestige/Centrality + Diversity Mathematical foundation: vertex-reinforced random walk Connections: • Polya’s Urn • Preferential Attachments • Word burstiness Why it works? • Rich-gets-richer • Local resource competition Future work: Query dependent DivRank;

  11. Thanks!

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