Spider Community Detection: Seeded Geodesic Expansion with Modularity-Guided Refinement and Greedy Merge Matching

Community detection plays a central role in understanding the modular structure of complex networks. This work introduces Spider Community Detection, a hybrid local–global algorithm that constructs communities through a depth-bounded geodesic expansion process. Each spider originates from a structurally strong seed node selected using a composite score based on degree, triangle participation, and local clustering. From each seed, the algorithm grows a localized spider-shaped subgraph through bounded breadth-first exploration, where candidate nodes are evaluated using true modularity gain together with a triangle-closure signal. After the initial spider construction, the method applies modularity-guided attachment of the remaining vertices, Louvain-style local refinement, and greedy merge matching under conductance constraints to reconcile local structure with global partition coherence. Experimental evaluations on real and benchmark datasets, including Karate Club, High School, Political Blogs, Cora, and DBLP, show that Spider produces partitions that are competitive with the established methods in terms of ground-truth recovery and structural quality, while yielding communities with sharp boundaries under conductance-sensitive evaluation.