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|Title:||Effective pruning techniques for mining quasi-cliques|
|Authors:||Liu, G. |
|Citation:||Liu, G.,Wong, L. (2008). Effective pruning techniques for mining quasi-cliques. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5212 LNAI (PART 2) : 33-49. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-540-87481-2_3|
|Abstract:||Many real-world datasets, such as biological networks and social networks, can be modeled as graphs. It is interesting to discover densely connected subgraphs from these graphs, as such subgraphs represent groups of objects sharing some common properties. Several algorithms have been proposed to mine quasi-cliques from undirected graphs, but they have not fully utilized the minimum degree constraint for pruning. In this paper, we propose an efficient algorithm called Quick to find maximal quasi-cliques from undirected graphs. The Quick algorithm uses several effective pruning techniques based on the degree of the vertices to prune unqualified vertices as early as possible, and these pruning techniques can be integrated into existing algorithms to improve their performance as well. Our experiment results show that Quick is orders of magnitude faster than previous work on mining quasi-cliques. © 2008 Springer-Verlag Berlin Heidelberg.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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