SASH: Decoding Community Structure in Graphs
This paper addresses the graph community detection problem by proposing a novel modeling and solution framework grounded in coding theory. It conceptualizes community structure as an underlying codeword to be recovered from a noisy graph, formally defining community-specific coding representations and associated parameters—thereby establishing the first theoretical linkage between community discovery and error-correcting coding theory. The authors introduce a coding mechanism based on the planted partition model and design an efficient decoding algorithm, SASH. Experiments on benchmark datasets—including the assortative planted partition model and the Zachary karate club network—demonstrate that SASH accurately identifies densely connected clusters and significantly improves partition quality. The core contributions are threefold: (1) the first formalization of community detection as a structured encoding/decoding problem; (2) the establishment of a cross-disciplinary theoretical bridge between graph mining and coding theory; and (3) a verifiable, scalable algorithmic implementation with strong empirical performance.