Leveraging Network Dynamics for Improved Link Prediction

TL;DR

This paper introduces RPM, a supervised framework that improves link prediction by modeling network dynamics through predicted link modification rates using time series data, outperforming traditional similarity-based methods.

Contribution

The paper presents RPM, a novel link prediction method that explicitly models network dynamics via rate prediction, enhancing prediction accuracy over existing similarity-based approaches.

Findings

RPM outperforms similarity measures in link prediction accuracy

Modeling network dynamics via rate prediction improves results

Using original link distribution enhances model training

Abstract

The aim of link prediction is to forecast connections that are most likely to occur in the future, based on examples of previously observed links. A key insight is that it is useful to explicitly model network dynamics, how frequently links are created or destroyed when doing link prediction. In this paper, we introduce a new supervised link prediction framework, RPM (Rate Prediction Model). In addition to network similarity measures, RPM uses the predicted rate of link modifications, modeled using time series data; it is implemented in Spark-ML and trained with the original link distribution, rather than a small balanced subset. We compare the use of this network dynamics model to directly creating time series of network similarity measures. Our experiments show that RPM, which leverages predicted rates, outperforms the use of network similarity measures, either individually or within a time series.