发表机构
Aalto University School of Science; Lockheed Martin（阿尔托大学理学院; 洛克希德·马丁公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有图神经网络在重叠社区检测中因局部消息传递导致的边界模糊和长程依赖缺失问题，提出DISCO框架，融合扩散结构先验、稀疏注意力与非负社区学习，在基准上表现优异，并在网络安全异常检测中验证了实用性。
AI 中文摘要
重叠社区的检测对于建模节点同时参与多个结构或功能组的网络至关重要。现有的图神经网络方法通常依赖于局部消息传递，这可能会通过平滑效应模糊社区边界，并限制对结构相关的长程依赖的表征。我们引入了扩散诱导的空间注意力社区检测（DISCO），这是一个深度学习框架，结合了源于影响传播动力学的结构先验、稀疏多头注意力以及非负社区归属学习。该先验识别出超越直接图邻居的候选交互，并根据其结构邻近性对注意力进行偏置，而伯努利-泊松边重建目标则使得能够从节点属性、结构特征或两者中推断重叠社区。基准实验表明，DISCO在不同输入配置下与已建立的图卷积和图注意力方法相比具有竞争力。为了展示其实用性，我们提出了一个概念验证的网络安全用例，其中从连续通信网络快照推断出的社区归属之间的变化提供了可解释的异常信号。时间社区相似性识别结构偏差，而节点级贡献有助于定位与之相关的设备。因此，DISCO既提供了一种灵活的重叠社区检测方法，也为分析动态网络中的结构变化奠定了基础。
英文摘要
Detection of overlapping communities is essential for modelling networks in which nodes participate simultaneously in multiple structural or functional groups. Existing graph neural network approaches commonly rely on local message passing, which can obscure community boundaries through smoothing and limit the representation of structurally relevant long-range dependencies. We introduce Diffusion-Induced Spatial Attention Community Detection (DISCO), a deep-learning framework that combines a structural prior derived from influence spreading dynamics, sparse multi-head attention, and non-negative community-affiliation learning. The prior identifies candidate interactions beyond immediate graph neighbours and biases attention according to their structural proximity, while a Bernoulli-Poisson edge-reconstruction objective enables overlapping community inference from node attributes and structural profiles, or both. Benchmark experiments show that DISCO performs competitively against established graph convolutional and graph attention approaches across different input configurations. To demonstrate its practical applicability, we present a proof-of-concept cybersecurity use case in which changes between community assignments inferred from consecutive communication-network snapshots provide an interpretable anomaly signal. Temporal community similarity identifies structural deviations, while node-level contributions help locate the devices associated with them. DISCO therefore provides both a flexible method for overlapping community detection and a foundation for analysing structural change in dynamic networks.
