A view of the EM algorithm that justifies incremental, sparse, and other variants
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Mining the network value of customers
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Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
ACM Transactions on Knowledge Discovery from Data (TKDD)
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
Prediction of Information Diffusion Probabilities for Independent Cascade Model
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Proceedings of the forty-first annual ACM symposium on Theory of computing
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
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
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A game-theoretic framework to identify overlapping communities in social networks
Data Mining and Knowledge Discovery
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Proceedings of the 20th international conference on World wide web
Particle competition and cooperation for uncovering network overlap community structure
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
An analysis of probabilistic methods for top-N recommendation in collaborative filtering
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Mining attribute-structure correlated patterns in large attributed graphs
Proceedings of the VLDB Endowment
Using content and interactions for discovering communities in social networks
Proceedings of the 21st international conference on World Wide Web
Chromatic correlation clustering
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards linear time overlapping community detection in social networks
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
The role of information diffusion in the evolution of social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic topic models for sequence data
Machine Learning
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Given a directed social graph and a set of past informa- tion cascades observed over the graph, we study the novel problem of detecting modules of the graph (communities of nodes), that also explain the cascades. Our key observation is that both information propagation and social ties forma- tion in a social network can be explained according to the same latent factor, which ultimately guide a user behavior within the network. Based on this observation, we propose the Community-Cascade Network (CCN) model, a stochas- tic mixture membership generative model that can fit, at the same time, the social graph and the observed set of cas- cades. Our model produces overlapping communities and for each node, its level of authority and passive interest in each community it belongs. For learning the parameters of the CCN model, we devise a Generalized Expectation Maximization procedure. We then apply our model to real-world social networks and in- formation cascades: the results witness the validity of the proposed CCN model, providing useful insights on its signif- icance for analyzing social behavior.