Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient influence maximization in social networks
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
A Combination Approach to Web User Profiling
ACM Transactions on Knowledge Discovery from Data (TKDD)
Topic level expertise search over heterogeneous networks
Machine Learning
Tree-structured conditional random fields for semantic annotation
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
A Unified Probabilistic Framework for Name Disambiguation in Digital Library
IEEE Transactions on Knowledge and Data Engineering
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Ranking structural parameters for social networks
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
Learning to diversify expert finding with subtopics
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Modeling topic hierarchies with the recursive chinese restaurant process
Proceedings of the 21st ACM international conference on Information and knowledge management
Probabilistic solutions of influence propagation on social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We study the problem of topic-level social network search, which aims to find who are the most influential users in a network on a specific topic and how the influential users connect with each other. We employ a topic model to find topical aspects of each user and a retrieval method to identify influential users by combining the language model and the topic model. An influence maximization algorithm is then presented to find the sub network that closely connects the influential users. Two demonstration systems have been developed and are online available. Empirical analysis based on the user's viewing time and the number of clicks validates the proposed methodologies.