Randomized algorithms
Proceedings of the 11th international conference on World Wide Web
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
PageSim: a novel link-based measure of web page aimilarity
Proceedings of the 15th international conference on World Wide Web
Generalizing PageRank: damping functions for link-based ranking algorithms
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Mining frequent cross-graph quasi-cliques
ACM Transactions on Knowledge Discovery from Data (TKDD)
Fast dynamic reranking in large graphs
Proceedings of the 18th international conference on World wide web
SNAKDD 2008 social network mining and analysis postworkshop report
ACM SIGKDD Explorations Newsletter
Estimating sharer reputation via social data calibration
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Ranking the centrality of a node within a graph is a fundamental problem in network analysis. Traditional centrality measures based on degree, betweenness, or closeness miss to capture the structural context of a node, which is caught by eigenvector centrality (EVC) measures. As a variant of EVC, PageRank is effective to model and measure the importance of web pages in the web graph, but it is problematic to apply it to other link-based ranking problems. In this paper, we propose a new influence propagation model to describe the propagation of pre-defined importance over individual nodes and groups accompanied with random walk paths, and we propose new IPRank algorithm for ranking both individuals and groups. We also allow users to define specific decay functions that provide flexibility to measure link-based centrality on different kinds of networks. We conducted testing using synthetic and real datasets, and experimental results show the effectiveness of our method.