摘要： In this article, we explore the relationships among digital objects in terms of their similarity based on vertex similarity measures. We argue that SimRank—a famous similarity measure—and its families, such as P-Rank and SimRank++, fail to capture similar node pairs in certain conditions, especially when two nodes can only reach each other through paths of odd lengths. We present new similarity measures ASCOS and ASCOS++ to address the problem. ASCOS outputs a more complete similarity score than SimRank and SimRank’s families. ASCOS++ enriches ASCOS to include edge weight into the measure, giving all edges and network weights an opportunity to make their contribution. We show that both ASCOS++ and ASCOS can be reformulated and applied on a distributed environment for parallel contribution. Experimental results show that ASCOS++ reports a better score than SimRank and several famous similarity measures. Finally, we re-examine previous use cases of SimRank, and explain appropriate and inappropriate use cases. We suggest future SimRank users following the rules proposed here before naïvely applying it. We also discuss the relationship between ASCOS++ and PageRank. 出版日期： 2015-10-26 出處： ACM transactions on knowledge discovery from data, 2015-10, Vol.10 (2), p.1-26 資源來源： ACM Digital Library Complete 識別號： ISSN: 1556-4681 識別號： EISSN: 1556-472X 識別號： DOI: 10.1145/2776894
