一种多结构及文本融合的网络表征方法

doi:10.11896／j.issn.1002-137X.2018.07.006

Abstract

Abstract: Network representation obtains the vector representations of nodes by deeply learning network structure,and mines the potential information on the network,which is an important method of reducing dimension in social computing.As for TADW,which is a network representation method based on matrix decomposition and combining text and structure,this paper first analyzed and discussed the influence of the location of text attributes matrix on network representation.Then,it proposed a social network representation method that incorporates relationship structure,interaction structure and textual attributes.Experimental results on multiple datasets show that the proposed method outperforms other classical network representation methods in classification tasks.

Key words: Matrix factorization, Multi-network structures, Representation learning, Social network

CLC Number:

TP181

LI Jia-yi, ZHAO Yu, WANG Li. Network Representation Model Based on Multi-architectures and Text Fusion[J].Computer Science, 2018, 45(7): 38-41.

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Network Representation Model Based on Multi-architectures and Text Fusion

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