Classifying molecular sequences using a linkage graph with their pairwise similarities
Theoretical Computer Science - Special issue: Genome informatics
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Massive Quasi-Clique Detection
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
Approximating a collection of frequent sets
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Extracting redundancy-aware top-k patterns
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The worst-case time complexity for generating all maximal cliques and computational experiments
Theoretical Computer Science - Computing and combinatorics
Effective Pruning Techniques for Mining Quasi-Cliques
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Note: A note on the problem of reporting maximal cliques
Theoretical Computer Science
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Efficient Computation of Diverse Query Results
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Top-k typicality queries and efficient query answering methods on large databases
The VLDB Journal — The International Journal on Very Large Data Bases
Finding maximal cliques in massive networks by H*-graph
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Efficient core decomposition in massive networks
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Triangle listing in massive networks and its applications
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding maximal cliques in massive networks
ACM Transactions on Database Systems (TODS)
Truss decomposition in massive networks
Proceedings of the VLDB Endowment
Fast algorithms for maximal clique enumeration with limited memory
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Triangle listing in massive networks
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
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Recent research efforts have made notable progress in improving the performance of (exhaustive) maximal clique enumeration (MCE). However, existing algorithms still suffer from exploring the huge search space of MCE. Furthermore, their results are often undesirable as many of the returned maximal cliques have large overlapping parts. This redundancy leads to problems in both computational efficiency and usefulness of MCE. In this paper, we aim at providing a concise and complete summary of the set of maximal cliques, which is useful to many applications. We propose the notion of τ-visible MCE to achieve this goal and design algorithms to realize the notion. Based on the refined output space, we further consider applications including an efficient computation of the top-k results with diversity and an interactive clique exploration process. Our experimental results demonstrate that our approach is capable of producing output of high usability and our algorithms achieve superior efficiency over classic MCE algorithms.