Models for Capturing Temporal Smoothness in Evolving Networks for Learning Latent Representation of Nodes
Abstract
In a dynamic network, the neighborhood of the vertices evolve across different temporal snapshots of the network. Accurate modeling of this temporal evolution can help solve complex tasks involving real-life social and interaction networks. However, existing models for learning latent representation are inadequate for obtaining the representation vectors of the vertices for different time-stamps of a dynamic network in a meaningful way. In this paper, we propose latent representation learning models for dynamic networks which overcome the above limitation by considering two different kinds of temporal smoothness: (i) retrofitted, and (ii) linear transformation. The retrofitted model tracks the representation vector of a vertex over time, facilitating vertex-based temporal analysis of a network. On the other hand, linear transformation based model provides a smooth transition operator which maps the representation vectors of all vertices from one temporal snapshot to the next (unobserved) snapshot-this facilitates prediction of the state of a network in a future time-stamp. We validate the performance of our proposed models by employing them for solving the temporal link prediction task. Experiments on 9 real-life networks from various domains validate that the proposed models are significantly better than the existing models for predicting the dynamics of an evolving network.
Cite
@article{arxiv.1804.05816,
title = {Models for Capturing Temporal Smoothness in Evolving Networks for Learning Latent Representation of Nodes},
author = {Tanay Kumar Saha and Thomas Williams and Mohammad Al Hasan and Shafiq Joty and Nicholas K. Varberg},
journal= {arXiv preprint arXiv:1804.05816},
year = {2018}
}