一种基于K近邻增强的属性网络聚类通用框架
A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation
AI总结：
针对多类型属性网络聚类中捕捉多跳连接的难题，提出基于K近邻增强的通用框架ANCKA，支持多种属性网络聚类任务，经大量实验验证其聚类质量与效率均优于现有方法。
摘要：
在节点属性中包含实体特定信息的属性网络，在社交网络、电子商务、生物信息学等领域的建模中无处不在。其固有网络拓扑结构涵盖从简单图到具有高阶交互的超图，再到具有独立层的多重图等多种形式。节点聚类是一项重要的图挖掘任务，旨在将属性网络的节点划分为k个不相交的簇，使得簇内节点连接紧密且属性相似，而簇间节点距离较远且属性相异。要在多种类型的属性网络上实现有效聚类，捕捉通过节点或属性形成的多跳连接极具挑战性。\n本文首先提出AHCKA，作为一种高效的属性超图聚类（Attributed Hypergraph Clustering, AHC）方法。AHCKA包含三项核心设计：一是精心设计的K近邻增强策略，用于在超图上优化利用属性信息；二是联合超图随机游走模型，用于构建有效的AHC目标函数；三是带有加速技术的高效求解器，用于目标函数的优化。\n由于所提出的技术可扩展至各类属性网络，我们进一步开发了ANCKA这一通用属性网络聚类框架，可支持属性图聚类（Attributed Graph Clustering, AGC）、属性多重图聚类（Attributed Multiplex Graph Clustering, AMGC）以及AHC任务。此外，我们还为ANCKA设计了适配GPU加速的算法架构以提升效率。\n我们开展了大量实验：在8个属性超图上与19种对比方法、6个属性图上与16种对比方法、3个属性多重图上与16种对比方法分别进行比较，所有实验结果均表明，本文方法具备卓越的聚类质量与效率。
英文摘要：
Attributed networks containing entity-specific information in node attributes are ubiquitous in modeling social networks, e-commerce, bioinformatics, etc. Their inherent network topology ranges from simple graphs to hypergraphs with high-order interactions and multiplex graphs with separate layers. An important graph mining task is node clustering, aiming to partition the nodes of an attributed network into k disjoint clusters such that intra-cluster nodes are closely connected and share similar attributes, while inter-cluster nodes are far apart and dissimilar. It is highly challenging to capture multi-hop connections via nodes or attributes for effective clustering on multiple types of attributed networks. In this paper, we first present AHCKA as an efficient approach to attributed hypergraph clustering (AHC). AHCKA includes a carefully-crafted K-nearest neighbor augmentation strategy for the optimized exploitation of attribute information on hypergraphs, a joint hypergraph random walk model to devise an effective AHC objective, and an efficient solver with speedup techniques for the objective optimization. The proposed techniques are extensible to various types of attributed networks, and thus, we develop ANCKA as a versatile attributed network clustering framework, capable of attributed graph clustering (AGC), attributed multiplex graph clustering (AMGC), and AHC. Moreover, we devise ANCKA with algorithmic designs tailored for GPU acceleration to boost efficiency. We have conducted extensive experiments to compare our methods with 19 competitors on 8 attributed hypergraphs, 16 competitors on 6 attributed graphs, and 16 competitors on 3 attributed multiplex graphs, all demonstrating the superb clustering quality and efficiency of our methods.
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