TY - GEN
T1 - Finding all maximal connected S-cliques in social networks
AU - Behar, Rachel
AU - Cohen, Sara
N1 - Publisher Copyright: © 2018 Copyright held by the owner/author(s)
PY - 2018
Y1 - 2018
N2 - Cliques are commonly used for social network analysis tasks, as they are a good representation of close-knit groups of people. For this reason (as well as for others), the problem of enumerating, i.e., finding, all maximal cliques in a graph has received extensive treatment. However, considering only complete subgraphs is too restrictive in many real-life scenarios where “almost cliques” may be even more useful. Hence, the notion of an s-clique, a clique relaxation that allows every node to be at distance at most s from every other node, has been introduced. Connected s-cliques add the natural requirement of connectivity to the notion of an s-clique. This paper presents efficient algorithms for finding all maximal connected s-cliques in a graph. We present a provably efficient algorithm, which runs in polynomial delay. In addition, we present several variants of the well-known Bron-Kerbosch algorithm for maximal clique generation. Extensive experimentation over both real and synthetic datasets shows the efficiency of our algorithms, and their scalability with respect to graph size, density, and choice of s.
AB - Cliques are commonly used for social network analysis tasks, as they are a good representation of close-knit groups of people. For this reason (as well as for others), the problem of enumerating, i.e., finding, all maximal cliques in a graph has received extensive treatment. However, considering only complete subgraphs is too restrictive in many real-life scenarios where “almost cliques” may be even more useful. Hence, the notion of an s-clique, a clique relaxation that allows every node to be at distance at most s from every other node, has been introduced. Connected s-cliques add the natural requirement of connectivity to the notion of an s-clique. This paper presents efficient algorithms for finding all maximal connected s-cliques in a graph. We present a provably efficient algorithm, which runs in polynomial delay. In addition, we present several variants of the well-known Bron-Kerbosch algorithm for maximal clique generation. Extensive experimentation over both real and synthetic datasets shows the efficiency of our algorithms, and their scalability with respect to graph size, density, and choice of s.
UR - http://www.scopus.com/inward/record.url?scp=85051517385&partnerID=8YFLogxK
U2 - https://doi.org/10.5441/002/edbt.2018.07
DO - https://doi.org/10.5441/002/edbt.2018.07
M3 - منشور من مؤتمر
T3 - Advances in Database Technology - EDBT
SP - 61
EP - 72
BT - Advances in Database Technology - EDBT 2018
A2 - Bohlen, Michael
A2 - Pichler, Reinhard
A2 - May, Norman
A2 - Rahm, Erhard
A2 - Wu, Shan-Hung
A2 - Hose, Katja
T2 - 21st International Conference on Extending Database Technology, EDBT 2018
Y2 - 26 March 2018 through 29 March 2018
ER -