On a Bernoulli Autoregression Framework for Link Discovery and Prediction

TL;DR

This paper introduces a Bernoulli autoregressive model for dynamic link prediction in time-dependent networks, leveraging auxiliary data and a scalable stochastic gradient approach for large networks.

Contribution

It proposes a novel Bernoulli autoregressive framework with regularization for link discovery and prediction, scalable to large networks, and applicable to real-world dynamic networks.

Findings

Effective on product-usage networks

Successful on Reddit sentiment data

Scalable to millions of nodes

Abstract

We present a dynamic prediction framework for binary sequences that is based on a Bernoulli generalization of the auto-regressive process. Our approach lends itself easily to variants of the standard link prediction problem for a sequence of time dependent networks. Focusing on this dynamic network link prediction/recommendation task, we propose a novel problem that exploits additional information via a much larger sequence of auxiliary networks and has important real-world relevance. To allow discovery of links that do not exist in the available data, our model estimation framework introduces a regularization term that presents a trade-off between the conventional link prediction and this discovery task. In contrast to existing work our stochastic gradient based estimation approach is highly efficient and can scale to networks with millions of nodes. We show extensive empirical results on both actual product-usage based time dependent networks and also present results on a Reddit based data set of time dependent sentiment sequences.