A Comparative Study of Representation Learning Techniques for Dynamic Networks

被引：1

作者：

Vazquez, Carlos Ortega ^{[1
]}

Mitrovic, Sandra ^{[1
]}

De Weerdt, Jochen ^{[1
]}

vanden Broucke, Seppe ^{[1
,2
]}

机构：

[1] Katholieke Univ Leuven, Res Ctr Informat Syst Engn LIRIS, Leuven, Belgium

[2] Univ Ghent, Dept Business Informat & Operat Management, Ghent, Belgium

来源：

TRENDS AND INNOVATIONS IN INFORMATION SYSTEMS AND TECHNOLOGIES, VOL 3 | 2020年 / 1161卷

关键词：

Dynamic networks; Representation learning; LINK-PREDICTION;

D O I：

10.1007/978-3-030-45697-9_51

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Representation Learning in dynamic networks has gained increasingly more attention due to its promising applicability. In the literature, we can find two popular approaches that have been adapted to dynamic networks: random-walk based techniques and graph-autoencoders. Despite the popularity, no work has compared them in well-know datasets. We fill this gap by using two link prediction settings that evaluate the techniques. We find standard node2vec, a random-walk method, outperforms the graph-autoencoders.

引用

页码：523 / 530

页数：8

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