Abstract：Similarity calculations have many real life applications. The research on similarity calculations have mainly been focused on static graphs with many similarity calculation models based on SimRank. In real life, many systems, such as communication networks, are modeled by temporal graphs. However, the traditional SimRank algorithm cannot be implemented in temporal graphs. Therefore, this study analyzes the similarity calculation problem for large temporal graphs. A temporal-aware SimRank (TaSimRank) algorithm was developed to compute the node similarity through an efficient iterative method based on the topological structure and time constraints of the graph. An approximate algorithm is then used to implement the similarity calculations using a tree-based index built by a random walk and the Monte Carlo method. The algorithm balances the calculational time and efficiency. Tests on real temporal graphs demonstrate the effectiveness and extensibility of these approaches.
苗壮, 袁野, 乔百友, 王一舒, 马玉亮, 王国仁. 面向大规模时序图SimRank的计算方法[J]. 清华大学学报（自然科学版）, 2018, 58(12): 1066-1071.
MIAO Zhuang, YUAN Ye, QIAO Baiyou, WANG Yishu, MA Yuliang, WANG Guoren. SimRank calculations for large temporal graphs. Journal of Tsinghua University(Science and Technology), 2018, 58(12): 1066-1071.
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