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Title: MEGAN: A Generative Adversarial Network for Multi-View Network Embedding
Data from many real-world applications can be nat- urally represented by multi-view networks where the different views encode different types of rela- tionships (e.g., friendship, shared interests in mu- sic, etc.) between real-world individuals or enti- ties. There is an urgent need for methods to ob- tain low-dimensional, information preserving and typically nonlinear embeddings of such multi-view networks. However, most of the work on multi- view learning focuses on data that lack a net- work structure, and most of the work on net- work embeddings has focused primarily on single- view networks. Against this background, we con- sider the multi-view network representation learn- ing problem, i.e., the problem of constructing low- dimensional information preserving embeddings of multi-view networks. Specifically, we investigate a novel Generative Adversarial Network (GAN) framework for Multi-View Network Embedding, namely MEGAN, aimed at preserving the informa- tion from the individual network views, while ac- counting for connectivity across (and hence com- plementarity of and correlations between) differ- ent views. The results of our experiments on two real-world multi-view data sets show that the em- beddings obtained using MEGAN outperform the state-of-the-art methods on node classification, link prediction and visualization tasks.
Authors:
; ; ; ;
Award ID(s):
1636795
Publication Date:
NSF-PAR ID:
10146602
Journal Name:
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Page Range or eLocation-ID:
https://www.ijcai.org/Proceedings/2019/0489.pdf
Sponsoring Org:
National Science Foundation
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