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Finding a team of Experts in Social Networks

Finding a team of Experts in Social Networks. Theodoros Lappas UC Riverside. Joint work with: Evimaria Terzi (IBM Almaden), Kun Liu (IBM Almaden). Motivation. “ How can I find a team of experts that can collaborate effectively in order to complete a given task? ”. Problem .

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Finding a team of Experts in Social Networks

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  1. Finding a team of Experts in Social Networks Theodoros Lappas UC Riverside Joint work with: Evimaria Terzi (IBM Almaden), Kun Liu (IBM Almaden)

  2. Motivation “ How can I find ateam of experts that cancollaborate effectively in order to complete a given task? ”

  3. Problem • Given a task and a set of experts organized in a network, find a subset of experts that can effectively perform the task • Task: set of required skills • Expert: an individual with a specific skill-set • Network: represents strength of relationships

  4. Expertise networks • Collaboration networks (e.g., DBLP graph, coauthor networks) • Organizational structure of companies • LinkedIn • Geographical (map) of experts

  5. Eleanor Eleanor David Cynthia {graphics,java,python} {graphics,java,python} {graphics} {graphics, java} Bob Alice Alice {python} {algorithms} {algorithms} What makes a team effective for a task? • T = {algorithms, java, graphics, python} Coverage:every required skill in T is included in the skill-set of at leastone team member

  6. Eleanor David Cynthia {graphics,java,python} {graphics} {graphics, java} Bob Alice {python} {algorithms} Is coverage enough? T={algorithms,java,graphics,python} A,E could perform the task if they could communicate A A D A,B,C form an effective group that can communicate B B C C E E Communication:the members of the team must be able to efficiently communicate and work together

  7. Problem definition • Given a task and a social network of individuals G, find the subset (team) of G that can effectivelyperform the given task. • Thesis: Good teams are teams that have the necessary skills and can also communicate effectively

  8. How to measure effective communication? The longest shortest path between any two nodes in the subgraph • Diameter of the subgraph defined by the group members A A D B B C C E E diameter = infty diameter = 1

  9. How to measure effective communication? The total weight of the edges of a tree that spans all the team nodes • MST (Minimum spanning tree) of the subgraph defined by the group members A A D B B C C E E MST = infty MST = 2

  10. Problem definition – v.1.1 • Given a task and a social network G of individuals, find the subset (team) of individuals that can perform the given task and define a subgraph in Gwith the minimum diameter. • Problem is NP-hard

  11. The RarestFirst algorithm T={algorithms,java,graphics,python} {graphics,python,java} {algorithms,graphics} A B A B Skills: algorithms graphics java python E E {algorithms,graphics,java} C D {python,java} {python} αrare= algorithms Srare={Bob, Eleanor} Diameter = 2

  12. The RarestFirst algorithm T={algorithms,java,graphics,python} {graphics,python,java} {algorithms,graphics} A B Skills: algorithms graphics java python E E {algorithms,graphics,java} C C D {python,java} {python} αrare= algorithms Srare={Bob, Eleanor} Diameter = 1 Running time: Quadratic to the number of nodes Approximation factor: 2xOPT

  13. Problem definition – v.1.2 • Given a task and a social network G of individuals, find the subset (team) of individuals that can perform the given task and define a subgraph in Gwith the minimum MST cost. • Problem is NP-hard Best known Approximation factor: O(log3n log k)

  14. The SteinerTree problem • Graph G(V,E) • Set of Required Vertices R • Find G’ subgraph of G such that G’ contains all the required vertices (R) and MST(G’) is minimized Required vertices

  15. The EnhancedSteiner algorithm T={algorithms,java,graphics,python} graphics {graphics,python,java} {algorithms,graphics} A B java algorithms E E {algorithms,graphics,java} D C D python {python,java} {python} MST Cost = 1

  16. Experiments

  17. Dataset DBLPDataset ( DM, AI, DB, T ) ~6000 authors Skills: keywords appearing in paper titles ~2000 features Social Network: Co-Authorship Graph Tasks: Subsets of keywords with different cardinality

  18. Cardinality of teams

  19. Example teams (I) • S. Brin, L. Page: The anatomy of a large-scale hypertextual Web search engine • Paolo Ferragina, Patrick Valduriez, H. V. Jagadish, Alon Y. Levy, Daniela Florescu, Divesh Srivastava, S. Muthukrishnan • P. Ferragina ,J. Han, H. V.Jagadish, Kevin Chen-Chuan Chang, A. Gulli, S. Muthukrishnan, Laks V. S. Lakshmanan

  20. Example teams (II) • J. Han, J. Pei, Y. Yin: Mining frequent patterns without candidate generation • F. Bonchi • A. Gionis, H. Mannila, R. Motwani

  21. Extensions • Other measures of effective communication • Other practical restrictions • Incorporate ability levels

  22. Thanks for your attention!

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