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arXiv Machine Learning

Community Concealment from Graph Neural Networks

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.

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arXiv Machine Learning
Sep 23

Diffusion-Induced Spatial Attention Overlapping Community Detection

The paper introduces DISCO, a deep‑learning framework for detecting overlapping communities in networks. DISCO integrates a diffusion‑based structural prior, sparse multi‑head attention, and a Bernoulli‑Poisson edge‑reconstruction objective to infer community affiliations from node attributes and structural profiles. Experiments show competitive performance against existing graph convolutional and attention methods, and a cybersecurity proof‑of‑concept demonstrates how community changes can signal anomalies in dynamic communication networks.

By Kosti Koistinen, Vesa Kuikka, Joni Herttuainen, Matthew Hendren, Brian Holt, Kimmo K. Kaski
diffusionbenchmarks
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arXiv Machine Learning
Sep 11

SynCo: Synthetic Community-Aware Attributed Graph Generator for Graph Neural Network Benchmarking

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
benchmarks
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arXiv Machine Learning
Sep 10

Not Just Oversmoothing: Detecting the Echo Chamber Effect in Graph Neural Networks

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
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arXiv Machine Learning
Aug 27

Are LLM-Enhanced GNNs Privacy-Safe?

The paper evaluates privacy risks in graph neural networks enhanced by large language models (LLMs). Using a five‑stage framework, the authors test six real‑world text‑attributed graph datasets with 42 model configurations and six privacy attack methods across link, label, and membership inference threats. Results show that LLM‑enhanced GNNs are more vulnerable than shallow baselines, with semantic enrichment amplifying exploitable signals, and that differential privacy can reduce risk but at a significant cost to utility.

By Longzhu He, Zelang Wen, Chaozhuo Li, Sen Su
llmsragsafety
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