GraphC: Parameter-free Hierarchical Clustering of Signed Graph Networks v2

TL;DR

GraphC is a scalable, parameter-free hierarchical clustering algorithm for signed graphs that effectively identifies communities without predefining cluster numbers, outperforming existing methods on large datasets.

Contribution

Introduces GraphC, a novel hierarchical clustering method for signed graphs that does not require preset cluster counts and demonstrates superior scalability and accuracy.

Findings

Outperforms ten baseline algorithms on fourteen datasets.

Successfully applied to large-scale Amazon-sourced signed graphs.

Achieves an average 18.64% improvement over the second-best method.

Abstract

Spectral clustering methodologies, when extended to accommodate signed graphs, have encountered notable limitations in effectively encapsulating inherent grouping relationships. Recent findings underscore a substantial deterioration in the efficacy of spectral clustering methods when applied to expansive signed networks. We introduce a scalable hierarchical Graph Clustering algorithm denominated GraphC. This algorithm excels at discerning optimal clusters within signed networks of varying magnitudes. GraphC aims to preserve the positive edge fractions within communities during partitioning while concurrently maximizing the negative edge fractions between communities. Importantly, GraphC does not require a predetermined cluster count (denoted as k). Empirical substantiation of GraphC 's efficacy is provided through a comprehensive evaluation involving fourteen datasets juxtaposed against ten baseline signed graph clustering algorithms. The algorithm's scalability is demonstrated through its application to extensive signed graphs drawn from Amazon-sourced datasets, each comprising tens of millions of vertices and edges. A noteworthy accomplishment is evidenced, with an average cumulative enhancement of 18.64% (consisting of the summation of positive edge fractions within communities and negative edge fractions between communities) over the second-best baseline for each respective signed graph. It is imperative to note that this evaluation excludes instances wherein all baseline algorithms failed to execute comprehensively.