Evaluate minimum-degree safeguards for thresholded priors

Determine how requiring every node to retain at least one incident edge when the diffusion-derived prior graph is thresholded affects overlapping community-detection performance.

Background

The diffusion-derived prior can fragment peripheral nodes when increasing link weights and thresholding the resulting spreading probabilities. The paper proposes a safeguard that would require every node to retain at least one incident edge, potentially reducing node isolation. However, the effect of this constraint on community-detection performance has not been evaluated.

References

One possible safeguard would be to require that every node retain at least one incident edge when the prior graph is thresholded. Such a constraint could reduce the risk of isolating peripheral nodes, but its effect on community-detection performance is included in our future work.

— Diffusion-Induced Spatial Attention Overlapping Community Detection  (2609.26737 - Koistinen et al., 22 Sep 2026) in Appendix A, “Influence-Spreading Model Weights”