1. Finding a team of Experts in Social Networks
Theodoros Lappas
UC Riverside
Joint work with: Evimaria Terzi (IBM Almaden), Kun Liu (IBM Almaden)
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2. Motivation
“ How can I find a team of experts that can collaborate effectively in order to complete a given task? ”
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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
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4. Expertise networks
Collaboration networks (e.g., DBLP graph, coauthor networks)
Organizational structure of companies
LinkedIn
Geographical (map) of experts
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5. What makes a team effective for a task?
T = {algorithms, java, graphics, python}
Alice
{algorithms}
Alice
{algorithms}
Bob
{python}
Cynthia
{graphics, java}
David
{graphics}
Eleanor
{graphics,java,python}
Eleanor
{graphics,java,python}
Coverage: every required skill in T is included in the skill-set
of at least one team member
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6. Is coverage enough? T={algorithms,java,graphics,python} A A D B B C C
Alice
{algorithms}
Bob
{python}
Cynthia
{graphics, java}
David
{graphics}
Eleanor
{graphics,java,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
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7. 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
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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
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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
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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 G with the minimum diameter.
Problem is NP-hard
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11. The RarestFirst algorithm
T={algorithms,java,graphics,python}
{graphics,python,java}
{algorithms,graphics} A A B B
Skills:
algorithms
graphics
java
python E E
{algorithms,graphics,java} C D
{python,java}
{python}
αrare= algorithms
Srare={Bob, Eleanor}
Diameter = 2
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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
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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 G with the minimum MST cost.
Problem is NP-hard
Best known Approximation factor: O(log3n log k)
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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
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15. The EnhancedSteiner algorithm
T={algorithms,java,graphics,python}
graphics
{graphics,python,java}
{algorithms,graphics}
java A B E
algorithms E
{algorithms,graphics,java} C D D
python
{python,java}
{python}
MST Cost = 1
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16. Experiments
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17. Dataset DBLP Dataset ( DM, AI, DB, T ) ~2000 features ~6000 authors
Skills: keywords appearing in paper titles
~2000 features
Social Network: Co-Authorship Graph
Tasks: Subsets of keywords with different cardinality
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18. Cardinality of teams
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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
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20. Example teams (II)
J. Han, J. Pei, Y. Yin: Mining frequent patterns without candidate generation
F. Bonchi
A. Gionis, H. Mannila, R. Motwani
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21. Extensions Other measures of effective communication
Other practical restrictions
Incorporate ability levels
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22. Thanks for your attention!
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