Embedding Identity and Interest for Social Networks

By Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu. 2017

17 citations recorded by the source corpus.

Network embedding fills the gap of applying tuple-based data mining models to networked datasets through learning latent representations or embeddings. However, it may not be likely to associate latent embeddings with physical meanings just as the name, latent embedding, literally suggests. Hence, models built on embeddings may not be interpretable. In this paper, we thus propose to learn identity embeddings and interest embeddings, where user identity includes demographic and affiliation information, and interest is demonstrated by activities or topics users are interested in. With identity and interest information, we can make data mining models not only more interpretable, but also more accurate, which is demonstrated on three real-world social networks in link prediction and multi-task classification.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Representation & Self-Supervised Learning

Citations in the corpus

• Inductive Representation Learning on Large Graphs (2017, neutral): “The majority of these approaches directly optimize the embeddings for each node using matrix-factorization-based objectives, and do not naturally generalize to unseen data, since they make predictions on nodes in a single, fixed graph [5, 11, 23, 28, 35, 36, 37, 39].”