Finding a Maximum Density Subgraph
Finding a Maximum Density Subgraph
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Ruling Out PTAS for Graph Min-Bisection, Dense k-Subgraph, and Bipartite Clique
SIAM Journal on Computing
PROJECT TEAM SELECTION USING FUZZY OPTIMIZATION APPROACH
Cybernetics and Systems
A team formation model based on knowledge and collaboration
Expert Systems with Applications: An International Journal
Finding a team of experts in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
IEEE Transactions on Image Processing
Discovering top-k teams of experts with/without a leader in social networks
Proceedings of the 20th ACM international conference on Information and knowledge management
Dense subgraphs with restrictions and applications to gene annotation graphs
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Online team formation in social networks
Proceedings of the 21st international conference on World Wide Web
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Given a task T, a set of experts V with multiple skills and a social network G(V, W) reflecting the compatibility among the experts, team formation is the problem of identifying a team C ? V that is both competent in performing the task T and compatible in working together. Existing methods for this problem make too restrictive assumptions and thus cannot model practical scenarios. The goal of this paper is to consider the team formation problem in a realistic setting and present a novel formulation based on densest subgraphs. Our formulation allows modeling of many natural requirements such as (i) inclusion of a designated team leader and/or a group of given experts, (ii) restriction of the size or more generally cost of the team (iii) enforcing locality of the team, e.g., in a geographical sense or social sense, etc. The proposed formulation leads to a generalized version of the classical densest subgraph problem with cardinality constraints (DSP), which is an NP hard problem and has many applications in social network analysis. In this paper, we present a new method for (approximately) solving the generalized DSP (GDSP). Our method, FORTE, is based on solving an equivalent continuous relaxation of GDSP. The solution found by our method has a quality guarantee and always satisfies the constraints of GDSP. Experiments show that the proposed formulation (GDSP) is useful in modeling a broader range of team formation problems and that our method produces more coherent and compact teams of high quality. We also show, with the help of an LP relaxation of GDSP, that our method gives close to optimal solutions to GDSP.