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Title: Semi-Supervised Deep Learning for Multiplex Networks
Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations, each relation representing a distinct layer. Such graphs are used to investigate many complex biological, social, and technological systems. In this work, we present a novel semi-supervised approach for structure-aware representation learning on multiplex networks. Our approach relies on maximizing the mutual information between local node-wise patch representations and label correlated structure-aware global graph representations to model the nodes and cluster structures jointly. Specifically, it leverages a novel cluster-aware, node-contextualized global graph summary generation strategy for effective joint-modeling of node and cluster representations across the layers of a multiplex network. Empirically, we demonstrate that the proposed architecture outperforms state-of-the-art methods in a range of tasks: classification, clustering, visualization, and similarity search on seven real-world multiplex networks for various experiment settings.  more » « less
Award ID(s):
2028944 2018627
NSF-PAR ID:
10295043
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
ACM SIGKDD Conference
Page Range / eLocation ID:
1234 to 1244
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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