Multilevel Attacks on Community Detection: From Global Deception to Individual Evasion

FSC-CD: A Feature–Structure Coupled Approach for Community Deception in Networks

Many real-world networks, such as social and biological networks, exhibit community structures. Community detection algorithms extract valuable insights from these networks by identifying densely connected groups, enabling applications such as recommendation, behavior understanding, and system optimization. However, growing concerns about data privacy and security have led to techniques that protect user information from being over-inferred within communities. This has given rise to community deception (CD), which introduces small, targeted perturbations to a network to obscure sensitive communities from detection algorithms. Most existing community deception approaches focus on modifying network topology, often neglecting the rich feature information embedded within communities. In this paper, we propose FSC-CD (Feature-Structure Coupled Community Deception), which couples feature-derived representations with structural cues to improve community concealment. FSC-CD is effective for both single-community deception and randomized multi-community hiding. A key innovation is a budget allocation strategy that optimizes the distribution of perturbations to maximize deception efficiency. Moreover, by exploiting feature similarity, FSC-CD designs an edge perturbation mechanism that improves stability under small perturbation budgets. Extensive experiments on three real-world network datasets across multiple community detectors show that FSC-CD is more stable and consistently outperforms baseline methods in hiding both single and multiple communities, reducing the detection accuracy by up to 17.6 % compared to state-of-the-art approaches.

Adversarial Distance Metrics: A Threat to Fairness in Clustering-Based Decision Systems

: Clustering algorithms are increasingly deployed in high-stakes decision-making systems under the assumption that transparent, explainable methods using standard distance metrics are inherently trustworthy. We challenge this assumption by demonstrating a configuration-based attack exploiting ε -semimetric distance functions, which satisfy nearly all properties of a topological metric while allowing arbitrary manipulation of pairwise distances. Building on the mathematical framework of ε -semimetrics introduced in prior work, we make three novel contributions: (1) we formalize a threat model in which an insider adversary manipulates the distance function to induce discriminatory clustering outcomes against demographic groups , exposing a gap in current fairness auditing practices that focus on data integrity and algorithm transparency but overlook configuration integrity; (2) we provide empirical feasibility analysis demonstrating successful attacks on K-means, DBSCAN, and agglomerative clustering with 100% manipulation accuracy for sample sizes up to m = 130, with construction cost O ( m 6 ) , and validate the attack on real census data (UCI Adult Income), demonstrating that Demographic Parity Difference increases from 0.200 to the theoretical maximum of 1.000 under attack; and (3) we propose concrete defenses including cryptographic metric commitment and statistical detection methods. The attack is most feasible for targeted discrimination affecting small groups ( m < 50, under 30 seconds), precisely where aggregate fairness statistics lack power to detect violations. Our findings demonstrate the urgent need to expand the scope of the algorithmic audit to include distance-function verification.

Comprehensive, Efficient Large-Scale Community Detection via Structural Entropy Game

A novel heuristic community detection algorithm, termed CoDeSEG, which identifies communities by minimizing the network's two-dimensional structural entropy within a potential game framework, and introduces a structural entropy-based node overlapping heuristic for detecting overlapping communities, with a near-linear time complexity.

Asymmetric dual-threshold bootstrap percolation on random hypergraphs

This work shows that a dual-threshold bootstrap percolation model on random hypergraphs separates a connected active backbone from large-scale endogenous activation, providing a basis for predicting cascade risk and designing targeted node- and group-level interventions in complex systems.

Community Detection as Adaptive Diffusion

Community detection in attributed networks faces a structural tension: classical graph algorithms scale efficiently but ignore semantic attributes, while Graph Neural Networks (GNNs) integrate semantics yet collapse under noise and quadratic memory bottlenecks. We introduce ECHO (Encoding Communities via High-Order Operators), a scalable, self-supervised framework that treats communities as regions of adaptive diffusion on semantic manifolds. ECHO couples a topology-aware encoder router, an attention-modulated diffusion operator, a memory-sharded contrastive objective, and a chunked \(\mathcal{O}(|E|+N\!\cdot\!k_{\max})\) extraction step. Across six standard attributed benchmarks, ECHO attains the best normalized mutual information on every assortative graph, improving over the strongest of nine recent baselines (including heterophily-specific GNNs and a graph transformer) by 5–29 NMI points on Amazon Photo/Computers, Coauthor-CS, and the 70-class CoraFull. Through a controlled feature-strength study we show precisely when the diffusion operator is decisive—it recovers communities from topology when node features are weak, while feature isolation is preferable when features are strong—and that the topology-aware router is what lets a single model span both regimes. Code: https://github.com/emilioferrara/ECHO-GNN

Beyond Isolated Phishing Emails: Discovering Hidden Campaign Relationships with MCDA