Conference Proceedings
Personalized PageRank to a Target Node, Revisited
Hanzhi Wang, Zhewei Wei, Junhao Gan, Sibo Wang, Zengfeng Huang
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining | ACM | Published : 2020
Abstract
Personalized PageRank (PPR) is a widely used node proximity measure in graph mining and network analysis. Given a source node s and a target node t, the PPR value π(s,t) represents the probability that a random walk from s terminates at t, and thus indicates the bidirectional importance between s and t. The majority of the existing work focuses on the single-source queries, which asks for the PPR value of a given source node s and every node t ∈ V. However, the single-source query only reflects the importance of each node t with respect to s. In this paper, we consider the single-target PPR query, which measures the opposite direction of importance for PPR. Given a target node t, the single-..
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Awarded by Australian Research Council
Funding Acknowledgements
This research is supported by National Natural Science Foundation of China (No. 61832017, No. 61972401, No. 61932001, No.U1936205), by Beijing Outstanding Young Scientist Program NO. BJJWZYJH012019100020098, and by the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China under Grant 18XNLG21. Junhao Gan is supported by Australian Research Council (ARC) DECRA DE190101118. Sibo Wang is also supported by Hong Kong RGC ECS Grant No. 24203419. Zengfeng Huang is supported by Shanghai Science and Technology Commission Grant No. 17JC1420200, and by Shanghai Sailing Program Grant No. 18YF1401200.