Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering large dense subgraphs in massive graphs
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Information flow modeling based on diffusion rate for prediction and ranking
Proceedings of the 16th international conference on World Wide Web
A spectral clustering approach to optimally combining numericalvectors with a modular network
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Managing and Mining Graph Data
Managing and Mining Graph Data
Randomization tests for distinguishing social influence and homophily effects
Proceedings of the 19th international conference on World wide web
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Influence and passivity in social media
Proceedings of the 20th international conference companion on World wide web
Information diffusion and external influence in networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding density-based subspace clusters in graphs with feature vectors
Data Mining and Knowledge Discovery
Learning the Strength of the Factors Influencing User Behavior in Online Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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The ever rising popularity of online social networks has not only attracted much attention from everyday users but also from academic researchers. In particular, research has been done to investigate the effect of social influence on users' actions on items in the network. However, all social influence research in the data-mining field has been done in a context-independent setting, i.e., irrespective of an item's characteristics. It would be interesting to find the specific contexts in which users influence each other in a similar manner. In this way, applications such as recommendation engines can focus on a specific context for making recommendations. In this paper, we pose the problem of finding contexts of social influence where the social influence is similar across all items in the context. We present a full-space clustering algorithm and a subspace clustering algorithm to find these contexts and test the algorithms on the Digg data set. We demonstrate that our algorithms are capable of finding meaningful contexts of influence in addition to rediscovering the predefined categories specific to the Digg news site.