The paper introduces FCom‑DICE, a feature‑aware perturbation method that rewires influential edges and adjusts node features to hide a target community from graph neural network (GNN) inference. It shows that concealment effectiveness depends on boundary connectivity and feature similarity, and that FCom‑DICE outperforms structure‑only DICE on synthetic and real networks such as Facebook, Wikipedia, and Bitcoin Transactions while preserving key structural and feature properties.
By Dalyapraz Manatova, Pablo Moriano, L. Jean Camp
The paper introduces the Echo Chamber Effect, a failure mode in Graph Neural Networks where intra-community representations collapse while inter-community separation remains, differing from traditional oversmoothing. It proposes the Echo Chamber Index (ECI) to detect this effect by stratifying pairwise distances by community membership. Building on this analysis, the authors present Community-Aware Split Propagation (CASP), a lightweight plugin that decouples intra- and inter-community aggregation and learns their balance from label structure, improving performance across various GNN backbones in both homophilic and heterophilic settings.
By Asela Hevapathige, Ahad N. Zehmakan, Asiri Wijesinghe, Saman Halgamuge
arXiv:2608. 06402v1 Announce Type: new Abstract: Community detection is a fundamental task in graph analytics that aims to identify cohesive groups of entities with similar behaviors or interests.
By Aoting Zeng, Kai Wang, Jianwei Wang, Yuxiang Sun, Yizhang He, Wenjie Zhang
arXiv:2607. 05469v1 Announce Type: cross Abstract: Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks.
By Jingyun Zhang, Hao Peng, Jianxin Li, Angsheng Li, Philip S. Yu
arXiv:2604. 07492v2 Announce Type: replace-cross Abstract: Message Passing Neural Networks have recently become the most popular approach to graph machine learning tasks; however, their receptive field is limited by the number of message passing layers.
By Oleg Platonov, Liudmila Prokhorenkova
SynCo is a synthetic graph generator that lets users control node degree distributions and sub‑community structures, addressing limitations of existing generators that rely on power‑law distributions and lack flexibility. It is evaluated on graph mimicking, hyperparameter tuning, and node clustering, outperforming state‑of‑the‑art methods while preserving original data distributions. SynCo can generate large graphs with up to 2.1 million nodes.
By Guilherme Henrique Messias, Mariana Caravanti de Souza, Sylvia Iasulaitis, Alan Dem\'etrius Baria Valejo
arXiv:2606. 09100v1 Announce Type: cross Abstract: Community detection is a fundamental problem in the analysis of complex networks.
By Shahin Momenzadeh, Rojiar Pir Mohammadiani
arXiv:2608. 03696v1 Announce Type: new Abstract: This work focuses on the problem of learning on temporal graphs, with particular emphasis on the task of clustering: obtaining coarse-grained representations by aggregating information from nodes, edges, and temporal dynamics - a task related to pooling in machine learning on graphs, or community detection in network science.
By Nelson Aloysio Reis de Almeida Passos, Emanuele Carlini, Salvatore Trani
The paper introduces a degree‑corrected joint matrix factorization technique for detecting communities in multilayer networks. It uses a nonnegative symmetric matrix trifactorization that enforces disjoint, shared communities across layers while allowing each layer to have distinct connectivity patterns and node degrees. An efficient algorithm is presented and evaluated on a multilayer degree‑corrected stochastic block model, showing superior performance compared to existing methods.
By Alexandra Dache, Manon Rustin, Arnaud Vandaele, Nicolas Gillis
arXiv:2605. 28209v2 Announce Type: replace Abstract: Graph clustering is essential in graph analysis for revealing structural patterns and node communities.
By Lei Zhang, Fubo Sun, Haipeng Yang, Zhong Guan, Likang Wu
The paper evaluates deep graph generative models against traditional network science models by comparing the topological similarity of generated networks to real-world networks and their effectiveness in identifying node immunization strategies for epidemic or misinformation spread. It finds that two deep graph generative models produce synthetic networks that closely resemble real-world structural properties, enabling them to identify effective immunization strategies.
By Tianrui Mao, Abele Malan, Megha Khosla, Lydia Chen, Huijuan Wang
arXiv:2507. 10005v2 Announce Type: replace Abstract: In recent years, graph-based machine learning techniques, such as reinforcement learning and graph neural networks, have garnered significant attention.
By Yash Arya, Sang Hoon Lee