Diffusion-Induced Spatial Attention Overlapping Community Detection
Detection of overlapping communities is essential for modelling networks in which nodes participate simultaneously in multiple structural or functional groups. Existing graph neural network approaches commonly rely on local message passing, which can obscure community boundaries through smoothing and limit the representation of structurally relevant long-range dependencies. We introduce Diffusion-Induced Spatial Attention Community Detection (DISCO), a deep-learning framework that combines a structural prior derived from influence spreading dynamics, sparse multi-head attention, and non-negative community-affiliation learning. The prior identifies candidate interactions beyond immediate g...
| Authors | Kosti Koistinen, Vesa Kuikka, Joni Herttuainen et al. |
|---|---|
| Primary category | cs.SI |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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