Algorithms for clustering data
Algorithms for clustering data
Solving the maximum clique problem using a tabu search approach
Annals of Operations Research - Special issue on Tabu search
Fully dynamic algorithms for maintaining shortest paths trees
Journal of Algorithms
Semi-Dynamic Shortest Paths and Breadth-First Search in Digraphs
STACS '97 Proceedings of the 14th Annual Symposium on Theoretical Aspects of Computer Science
Fully Dynamic Algorithms for Maintaining All-Pairs Shortest Paths and Transitive Closure in Digraphs
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
On clusterings: Good, bad and spectral
Journal of the ACM (JACM)
A new approach to dynamic all pairs shortest paths
Journal of the ACM (JACM)
Finding Friend Groups in Blogosphere
AINAW '08 Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops
A measure & conquer approach for the analysis of exact algorithms
Journal of the ACM (JACM)
Approximating maximum diameter-bounded subgraphs
LATIN'10 Proceedings of the 9th Latin American conference on Theoretical Informatics
Parameterized algorithmics and computational experiments for finding 2-clubs
IPEC'12 Proceedings of the 7th international conference on Parameterized and Exact Computation
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Finding cohesive subgroups is an important issue in studying social networks. Many models exist for defining cohesive subgraphs in social networks, such as clique, $$k$$-clique, and $$k$$-clan. The concept of $$k$$-club is one of them. A $$k$$-club of a graph is a maximal subset of the vertex set which induces a subgraph of diameter $$k$$. It is a relaxation of a clique, which induces a subgraph of diameter $$1$$. We conducted algorithmic studies on finding a $$k$$-club of size as large as possible. In this paper, we show that one can find a $$k$$-club of maximum size in $$O^{*}(1.62^n)$$ time where $$n$$ is the number of vertices of the input graph. We implemented a combinatorial branch-and-bound algorithm that finds a $$k$$-club of maximum size and a new heuristic algorithm called IDROP given in this paper. To speed up the programs, we introduce a dynamic data structure called $$k$$-DN which, under deletion of vertices from a graph, maintains for a given vertex $$v$$ the set of vertices at distances at most $$k$$. From the experimental results that we obtained, we concluded that a $$k$$-club of maximum size can be easily found in sparse graphs and dense graphs. Our heuristic algorithm finds, within reasonable time, $$k$$-clubs of maximum size in most of experimental instances. The gap between the size of a $$k$$-club of maximum size and a $$k$$-club found by IDROP is a constant for the number of vertices that we are able to test.