🤖 AI Summary
This work proposes TopKGraphs, a non-parametric and interpretable method for robust node similarity estimation in sparse, noisy, or heterogeneous networks. By anchoring random walks at source nodes and guiding transitions via Jaccard similarity, the approach treats walks as local neighborhood samplers. It generates partial node rankings and constructs an affinity matrix through robust rank aggregation, effectively integrating local structural cues with global walk-based information. Notably, TopKGraphs operates without relying on stationary distributions or learned embeddings. Extensive evaluations demonstrate its superior performance over established baselines—including Jaccard, Dice, personalized PageRank, and Node2Vec—across synthetic graphs, k-nearest neighbor graphs, and protein–protein interaction networks, highlighting its accuracy and robustness under diverse and challenging conditions.
📝 Abstract
Estimating node similarity is a fundamental task in network analysis and graph-based machine learning, with applications in clustering, community detection, classification, and recommendation. We propose TopKGraphs, a method based on start-node-anchored random walks that bias transitions toward nodes with structurally similar neighborhoods, measured via Jaccard similarity. Rather than computing stationary distributions, walks are treated as stochastic neighborhood samplers, producing partial node rankings that are aggregated using robust rank aggregation to construct interpretable node-to-node affinity matrices. TopKGraphs provides a non-parametric, interpretable, and general-purpose representation of node similarity that can be applied in both network analysis and machine learning workflows. We evaluate the method on synthetic graphs (stochastic block models, Lancichinetti-Fortunato-Radicchi benchmark graphs), k-nearest-neighbor graphs from tabular datasets, and a curated high-confidence protein-protein interaction network. Across all scenarios, TopKGraphs achieves competitive or superior performance compared to standard similarity measures (Jaccard, Dice), a diffusion-based method (personalized PageRank), and an embedding-based approach (Node2Vec), demonstrating robustness in sparse, noisy, or heterogeneous networks. These results suggest that TopKGraphs is a versatile and interpretable tool for bridging simple local similarity measures with more complex embedding-based approaches, facilitating both data mining and network analysis applications.