Abstract

Network embedding, which aims to learn low-dimensional representations of nodes, has been used for various graph related tasks including visualization, link prediction, and node classification. Most existing embedding methods rely solely on network structure. However, in practice, we often have auxiliary information about the nodes and/or their interactions, e.g., the content of scientific papers in coauthorship networks, or topics of communication in Twitter mention networks. Here, we propose a novel embedding method that uses both network structure and edge attributes to learn better network representations. Our method jointly minimizes the reconstruction error for higher order node neighborhood, social roles, and edge attributes using a deep architecture that can adequately capture highly nonlinear interactions. We demonstrate the efficacy of our model over existing state-of-the-art methods on a variety of real …

Date

November 15, 2018

Authors

Palash Goyal, Homa Hosseinmardi, Emilio Ferrara, Aram Galstyan

Journal

IEEE Transactions on Computational Social Systems

Volume

5

Issue

4

Pages

907-917

Publisher

IEEE