Arboricity and bipartite subgraph listing algorithms
Information Processing Letters
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Consensus algorithms for the generation of all maximal bicliques
Discrete Applied Mathematics - The fourth international colloquium on graphs and optimisation (GO-IV)
Efficient mining of iterative patterns for software specification discovery
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Community Mining from Signed Social Networks
IEEE Transactions on Knowledge and Data Engineering
A heuristic clustering algorithm for mining communities in signed networks
Journal of Computer Science and Technology
On ranking controversies in wikipedia: models and evaluation
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Efficient Mining of Closed Repetitive Gapped Subsequences from a Sequence Database
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Mining direct antagonistic communities in explicit trust networks
Proceedings of the 20th ACM international conference on Information and knowledge management
Community mining from multi-relational networks
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Mining antagonistic communities from social networks
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
An efficient algorithm for community mining with overlap in social networks
Expert Systems with Applications: An International Journal
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Social networks provide a wealth of data to study relationship dynamics among people. Most social networks such as Epinions and Facebook allow users to declare trusts or friendships with other users. Some of them also allow users to declare distrusts or negative relationships. When both positive and negative links co-exist in a network, some interesting community structures can be studied. In this work, we mine Direct Antagonistic Communities (DACs) within such signed networks. Each DAC consists of two sub-communities with positive relationships among members of each sub-community, and negative relationships among members of the other sub-community. Identifying direct antagonistic communities is an important step to understand the nature of the formation, dissolution, and evolution of such communities. Knowledge about antagonistic communities allows us to better understand and explain behaviors of users in the communities. Identifying DACs from a large signed network is however challenging as various combinations of user sets, which is very large in number, need to be checked. We propose an efficient data mining solution that leverages the properties of DACs, and combines the identification of strongest connected components and bi-clique mining. We have experimented our approach on synthetic, myGamma, and Epinions datasets to showcase the efficiency and utility of our proposed approach. We show that we can mine DACs in less than 15min from a signed network of myGamma, which is a mobile social networking site, consisting of 600,000 members and 8million links. An investigation on the behavior of users participating in DACs shows that antagonism significantly affects the way people behave and interact with one another.