1. Expert Team Formation made by Zhang, Cheng Social Network Data Analytics[M] Charu C. Aggarwal

2. made by Zhang, Cheng Outline Introduction Metrics Forming Teams of Experts

3. made by Zhang, Cheng Outline Introduction Metrics Forming Teams of Experts

4. made by Zhang, Cheng Introduction The objective is to find a group of experts in the network who can collectively perform a task in an effective manner. Skills are the most parts discussed. Apart from skills, the communication or collaboration cost(the overhead of team members working together) also plays an important role in the overall performance of the team. The cost is influenced by various factors including the personality of individual team members and their social connections.

5. made by Zhang, Cheng Outline Introduction Metrics Forming Teams of Experts

6. made by Zhang, Cheng Metrics Many methodologies have been developed to measure the personality of an individual and social connection. Personality: The Myers-Briggs Type Indicator (MBTI) Herrmann Brain Dominance Instrument (HBDI) Kolbe Conative Index (KCI) Social metrics: Diameter of the graph The cost of the minimum spanning tree of the graph

7. made by Zhang, Cheng The Myers-Briggs Type Indicator (MBTI) MBTI is a frequently-employed personality assessment tool. It can help people to identify the sort of jobs where they would be most comfortable and effective This metric can be used improving and predicting team performance[1] and evaluate candidates’ interpersonal relationships as team members[2].

8. made by Zhang, Cheng Herrmann Brain Dominance Instrument (HBDI) HBDI is another index that reflects individual’s affinity for creativity, facts, form, and feelings

9. made by Zhang, Cheng Kolbe Conative Index (KCI) KCI is a psychometric system that measures conation i.e. the way an individual instinctively approaches problem solving, arranges ideas or objects, and uses time or energy The personality metrics are great but they are more suitable for survey or questionnaire. Social metrics have also been studied. Lappas et al. [3] focused on social graph by means of diameter of the graph and the cost of minimum spanning tree to measure the communication overhead of the team.

10. made by Zhang, Cheng Outline Introduction Metrics Forming Teams of Experts

11. Motivation “ How can I find a team of experts that can collaborate effectively in order to complete a given task? ”

12. 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

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

14. 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 least one team member A lice {algorithms} B ob {python} C ynthia {graphics, java} D avid {graphics} E leanor {graphics,java,python} A lice {algorithms} E leanor {graphics,java,python}

15. Is coverage enough? Communication: the members of the team must be able to efficiently communicate and work together B ob {python} C ynthia {graphics, java} D avid {graphics} A lice {algorithms} E leanor {graphics,java,python} A BCE D T={algorithms,java,graphics,python} A ECB A,E could perform the task if they could communicate A,B,C form an effective group that can communicate

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

17. How to measure effective communication? Diameter of the subgraph defined by the group members A BCE DA ECB The longest shortest path between any two nodes in the subgraph diameter = inftydiameter = 1

18. How to measure effective communication? MST (Minimum spanning tree) of the subgraph defined by the group members A BCE DA ECB The total weight of the edges of a tree that spans all the team nodes MST = inftyMST = 2

19. 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 G with the minimum diameter. Problem is NP-hard

21. The RarestFirst algorithm AB C E D T={ algorithms,java,graphics,python } { graphics,python,java } {algorithms,graphics} {algorithms,graphics,java} α rare = algorithms S rare ={Bob, Eleanor} {python,java}{python} B E A Skills: algorithms graphics java python Diameter = 2

22. The RarestFirst algorithm AB C E D T={algorithms,java,graphics,python} {graphics,python,java} {algorithms,graphics} {algorithms,graphics,java} {python,java}{python} α rare = algorithms S rare ={Bob, Eleanor} E Skills: algorithms graphics java python Diameter = 1 C Running time: Quadratic to the number of nodes Approximation factor: 2xOPT

23. 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 G with the minimum MST cost. Problem is NP-hard Best known Approximation factor: O(log 3 n log k)

24. 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

26. The EnhancedSteiner algorithm AB C E D T={algorithms,java,graphics,python} {graphics,python,java} {algorithms,graphics} {algorithms,graphics,java} {python,java}{python} python java graphics algorithms E D MST Cost = 1

27. Experiments

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

29. Cardinality of teams

30. 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

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

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

33. made by Zhang, Cheng Reference [1] Shi-Jie Chen and Li Lin. Modeling team member characteristics for the formation of a multifunctional team in concurrent engineering. IEEE Transactions on Engineering Management, 51(2):111–124, 2004. [2] A.L. Hammer and G.E. Huszczo. Teams. Consulting Psychologists Press,1996. [3] Theodoros Lappas, Kun Liu, and Evimaria Terzi. Finding a team of experts in social networks. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09), pages 467–476, Paris, France, 2009.