Social Network Mining Based on Multi-source Information Fusion

doi:10.3969/j.issn.1671-1122.2017.09.017

Netinfo Security ›› 2017, Vol. 17 ›› Issue (9): 73-76.doi: 10.3969/j.issn.1671-1122.2017.09.017

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Social Network Mining Based on Multi-source Information Fusion

Jingjie MO^1,²(), Chenyang TU^1,², Jia PENG^1,², Jun YUAN^1,²

1. Data Assurance and Communication Security Research Center, Chinese Academy of Sciences, Beijing 100093, China
2. State Key Laboratory of Information Security, Beijing 100093, China

Received:2017-08-01 Online:2017-09-20 Published:2020-05-12

Abstract

Abstract:

This paper proposes a creative method for network embedding based on multi-source information fusion, which use DNGR model to represent structure information, and proposes a new model called DLDA to represent semantic information instead of LDA model because LDA model can’t describe the semantic similarity between the nodes, and use SDAE model for information fusion in the end. This paper evaluate the method on real-world datasets by applying it to the task of link prediction, community detection and abnormal nodes detection, which are the classic task in social network mining, and prove the effectiveness of our method.

Key words: multi-source information fusion, network embedding, autoencoder, social network mining

CLC Number:

TP309.1

Jingjie MO, Chenyang TU, Jia PENG, Jun YUAN. Social Network Mining Based on Multi-source Information Fusion[J]. Netinfo Security, 2017, 17(9): 73-76.

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References 12

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Social Network Mining Based on Multi-source Information Fusion

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