Experimental evaluation of the effect of community structures on link prediction

Abstract

Link prediction involves assessing the likelihood of connections between node pairs based on various structural properties. The effectiveness of link predictors can be influenced by complex structures such as communities. Since the community structure itself has different properties that describes its characteristics, measuring the impact of these properties on the performance of link predictors presents a challenge. In this work, we aim to uncover the role of community properties and the identification of community structures on the performance of link predictors. We propose a comprehensive experimental setup to evaluate the performance of twenty-nine link predictors on real-world networks with diverse topological features, as well as on synthetic networks where we control community-dependent properties such as cohesiveness and size. We assess the performance differences between network-wide and per-community link prediction to determine whether identifying communities aids in link prediction. The results indicate that link prediction is more accurate in networks with well-defined, disjoint communities, even when these communities are not explicitly identified. Additionally, the size of the communities can influence link prediction performance if the communities are identified.