CFLP: A new cost based feature for link prediction in dynamic networks

Abstract

Dynamic networks are social networks in which node-to-node links contain a temporal component, i.e., node-to-node interactions over a specific time interval. As a result, the dynamic network’s structure changes over time, and previously connected nodes may or may not have an edge connecting them at any one time. The link prediction issue in dynamic networks aims to identify future network linkages based on the relative behavior of previous network updates. We present a feature-based solution that considers both individual snapshots and the overall network throughout the full-time span to answer the link prediction problem. We present a novel feature called Cost-based feature for link prediction (CFLP) for estimating edge behavior throughout the entire network, which uses a reward and penalty structure to summarize node activity across the entire network. We use similarity indices, classified into four major categories: local similarity, global similarity, quasi-local similarity, and clustering coefficient-based similarity, to measure edge activity in individual snapshots. We have also selected fourteen different snapshot-based features to find the most excellent combination of minimum features for link prediction. We used regression and mutual information-based scoring for feature selection to correctly quantify the relative effect of features among themselves and the overall link prediction problem. In order to give the best feasible solution to the link prediction problem, these individual features and their combinations were examined with five machine learning models. We employed five performance matrices – AUC, AUPR, Average Precision, F1, and Balance Accuracy Score – to compare the performance of our method to those of state-of-the-art approaches, and found that our method outperformed all.