RECYCLE NET: CYCLE-AWARE, FEATURE-FREE GNN FOR COMMUNITY DETECTION

Community detection is a fundamental problem in network science, yet classical methods suffer from resolution limits and limited adaptability, while many Graph Neural Networks (GNNs) do not explicitly model higher-order cyclic structures that underlie real-world communities. We propose ReCycle Net (RCN), a feature-free, cycle-aware GNN that integrates Renewal Non-Backtracking Random Walk (RNBRW) reinforcement into a GAT-style backbone and is trained with a multi-term unsupervised objective combining modularity, Laplacian smoothness, contrastive consistency, and orthogonality regularization. RCN targets graphs where cyclic closure is structurally informative for community formation and learns embeddings that support unsupervised community recovery. Across standard benchmarks, RCN is competitive with strong baselines (e.g., PolBooks: NMI 0.60, ARI 0.67; Facebook: silhouette 0.85), with its clearest gains appearing on overlapping protein complexes under overlap-aware evaluation (Complex Portal: ONMI 0.344 at r = 2 vs. 0.243, 0.232, and 0.140 for generative overlapping-based, strong attention-based, and modularity-based baseline). On graphs with weaker cyclic closure (e.g., Cora), gains are smaller and RCN remains comparable to standard baselines. Overall, these results suggest that explicitly incorporating cycle-derived structure into GNN learning can be beneficial in cycle-rich regimes while remaining robust outside this setting.