Information Extraction to Improve Link Prediction in Scientific Social Network

被引：0

作者：

Stroele, Victor ^{[1
]}

Campos, Fernanda ^{[1
]}

Pereira, Crystiam K. ^{[1
]}

Zimbrao, Geraldo ^{[2
]}

Souza, Jano M. ^{[2
]}

机构：

[1] UFJF Fed Univ Juiz de Fora, PGCC Postgrad Program Comp Sci, Juiz De Fora, Brazil

[2] UFRJ Fed Univ Rio de Janeiro, COPPE, Syst Engn & Comp Program, Rio De Janeiro, Brazil

来源：

2016 IEEE 20TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD) | 2016年

关键词：

Information Extraction; Link Prediction; Scientific Social Network;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Link Prediction is a classic social networks analysis problem. Knowing in advance future actions in social network can help, for example, agents decision. Link Prediction techniques are based on metrics that have different approaches. In this paper, we model a multi-relational scientific social network to assess the impact of content extraction on topological metrics. Thus, a metric composed of topological and semantic approach is proposed to solve link prediction problem. The results were compared with those presented by Katz metric.

引用

页码：515 / 520

页数：6

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