Second-Order Markov Assumption Based Bayes Classifier for Networked Data With Heterophily

作     者：Dong, Sa Liu, Dayou Ouyang, Ruochuan Zhu, Yungang Li, Lina Li, Tingting Liu, Jie 

作者机构：Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Jilin Peoples R China Jilin Univ Big Data & Network Management Ctr Changchun 130012 Jilin Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2019年第7卷

页      面：34153-34161页

核心收录：

基　　金：National Natural Science Foundation of China [61502198  61572226  61472161] 

主　　题：Artificial intelligence data mining heterophilous networks machine learning networked data classification relational classifier 

摘      要：The classification of networked data is an interesting and challenging problem. Most traditional relational classifiers that are based on the principle of homophily have an unsatisfactory classification performance in networks with heterophily. This is because these methods treat inhomogeneous networks homogeneously. A progression of a network-only Bayes-classifier-based second-order Markov assumption is proposed for heterophilous networks in this paper to address this problem. First, we estimate the class distribution of an unlabeled node according to the class distribution of its neighbors neighbors. In this process, we perform this computation on the known and unknown neighbors separately. Second, we combine the two parts using multinomial na i ve Bayesian classification. Meanwhile, we pair a relaxation labeling collective inference method (which imports simulated annealing) with this new method to update the class distributions at each iteration. Comparisons of the experimental results demonstrate that the proposed method performs better when the networks are heterophilous.