Measuring node similarity using minimum cycles in networks

TL;DR

This paper introduces a novel node similarity measure based on minimal simple cycles in networks, demonstrating its effectiveness in link prediction and community detection tasks across various datasets.

Contribution

The paper proposes a new cycle-based node similarity metric with an edge-addition correction strategy, enhancing link prediction and community detection performance.

Findings

Cycle similarity improves link prediction accuracy.

Edge-addition correction enhances performance on real datasets.

Cyclic structure significance influences community detection outcomes.

Abstract

Cycles are ubiquitous in various networks such as social, biological, and technological systems, where they play a significant functional and dynamical role. This paper proposes a node similarity measure based on minimal simple cycles, referred to as cycle similarity. Specifically, the metric quantifies the similarity between two nodes by considering the minimal cycles that connect them through their neighboring nodes, with an upper bound imposed on the cycle size to ensure computational feasibility. We then systematically examine the effectiveness and applicability of this similarity measure through two fundamental tasks: link prediction and community detection. To address the scarcity of cycles in link prediction, an edge-addition correction strategy is introduced, whereby the existence of a candidate edge is hypothetically assumed before computing node similarity. Experimental results demonstrate that this correction leads to improved performance on datasets including karate, INT, PPI, and Grid. In hierarchical community detection using cycle similarity, we find that the significance of cyclic structures (reflected by Z-scores), the presence of pendant nodes with degree one, and the existence of cut vertices are the primary factors influencing the algorithm's performance.