文献类型：期刊文献

英文题名：A Graph Entropy Measure From Urelement to Higher-Order Graphlets for Network Analysis

作者：Huang, Ru[1];Chen, Zijian[1];Zhai, Guangtao[2];He, Jianhua[3];Chu, Xiaoli[4]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Inst Image Commun & Informat Proc, Shanghai 200240, Peoples R China;[3]Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, England;[4]Univ Sheffield, Dept Elect & Elect Engn, Sheffield S1 3JD, England

年份：2023

卷号：10

期号：2

起止页码：631

外文期刊名：IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING

收录：;EI(收录号：20224513092243);WOS:【SCI-EXPANDED(收录号:WOS:000966741100001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grants 61673178 and 61922063, in part by the Natural Science Foundation of Shanghai under Grant 20ZR1413800, and in part by the European Union's Horizon 2020research and innovation programme under the Marie Sklodowska-Curie under Grants 824019 and 101022280.

语种：英文

外文关键词：Graph entropy; graphlet estimation; graph characterization; higher-order graphlets; induced subgraphs

摘要：Graph entropy measures have recently gained wide attention for identifying and discriminating various networks in biology, society, transportation, etc. However, existing methods cannot sufficiently explore the structural contents by merely considering the elementary invariants of a graph, ignoring the underlying patterns in higher-order features. In this paper, we propose a general entropy-based graph representation framework (Greet) based on four pertinent properties of graphlet topology from urelement to higher-order statistics. Specifically, we introduce an unbiased graphlet estimation strategy for obtaining both urelement and higher-order statistics. Additionally, we define a novel family of information functions based on hierarchical topological features to compute the graph entropy, then construct a graph information entropy (GIE) vector using the obtained local and global structural statistics to facilitate downstream tasks. Furthermore, there are some advantages that our Greet exhibits over other methods: (a) high accuracy with < 1% relative error; (b) scalable for even larger vertex graphlets; (c) efficient calculation procedure with feasible speedup. Extensive experiments show that Greet exhibits superior performance on graph classification and clustering tasks, achieving remarkable improvements compared to several baselines. Altogether these findings pave the way for a wide range of applications of graphlet-based entropy as a complexity metric in graph analysis.