However, when we looked at the broader community structure, we found that the single-file movement data and traditional methods converged on similar estimates. This suggests that single-file movement data might be useful for coarse estimation of group-level structure #communitydetection 6/9

Our new Local #CommunityDetection in #DynamicGraphs Using #PersonalizedCentrality
http://bit.ly/2vJEOCl
@Algorithms_MDPI @MDPIOpenAccess

Well-connectedness of communities: Park et al show that many communities detected with standard algorithms are not well-connected: by cutting a few edges, such a community breaks into 2. Remedy by post-processing.

https://doi.org/10.1371/journal.pcsy.0000009

#clustering #communitydetection

DynBenchmark, shown at FRCCS 2025 (May, Bordeaux, pp 74‑85), offers Python libraries and visualization tools to build temporal networks for community‑detection tests. https://getnews.me/dynbenchmark-provides-customizable-benchmarks-for-community-tracking/ #dynbenchmark #communitydetection

A study of six GNN models found the unsupervised DMoN most stable under adversarial attacks, while supervised models lose accuracy when edges are deleted. Read more: https://getnews.me/robustness-of-graph-neural-networks-for-community-detection/ #gnn #communitydetection #robustness

Research proves low‑degree polynomial estimators fail below a revised threshold for SBMs with >√n communities, but counting cliques enables recovery above it. https://getnews.me/phase-transition-for-stochastic-block-models-with-many-communities/ #stochasticblockmodel #communitydetection

Researchers found a network‑embedding algorithm fully captures a graph when its mapping is invertible; otherwise it loses edge‑density information, hurting detection. Read more: https://getnews.me/study-reveals-information-loss-in-network-embedding-techniques/ #networkembedding #communitydetection

Community Detection on Model Explanation Graphs for Explainable AI
Ehsan Moradi
Paper
Details
#ExplainableAI #ModelExplanationGraphs #CommunityDetection