Robust Hierarchical Overlapping Community Detection With Personalized PageRank

Abstract

Community detection is a fundamental task in graph mining. Despite the fact that most of existing community detection methods are devoted to finding disjoint community structure, communities often overlap with each other and are recursively organized in a hierarchical structure in many realworld networks. Also, finding hierarchical overlapping community structure has significant implications in many real-world applications. Some of the few existing attempts suffer from the problem that the obtained community structure is sensitive to network changes as they are based heavily on one-hop node proximity to detect communities. To tackle this problem, we propose a robust hierarchical overlapping community detection method with Personalized PageRank (PPR), which is often regarded as a prevalent metric to measure node proximity globally. Specifically, motivated by the agglomerative hierarchical clustering method, we present an effective and efficient mechanism to merge small communities and form hierarchically organized overlapping communities. Experimental results on both synthetic and real-world networks corroborate the effectiveness and robustness of the proposed framework. In addition, we introduce how to make use of the detected community structure to perform various node proximity queries such as the top-k structural hole spanner query and the top-k heterogeneous node query, which can help us gain more insights on the underlying network.

pygrank: A Python package for graph node ranking

“…Various graph analysis approaches have been developed, with node ranking algorithms being a powerful option when scoring nodes based on link structure and prior scores. Node ranking techniques include spectral graph filters [1], which can see use in community detection [2,3], link prediction [4,5], and graph neural networks [6,7]. However, most approaches employ algorithms and parameters with little to no ablation studies, when different choices could better match the structure of analyzed or -in case of deployed tools -new graphs.…”

Toward an Adaptive Skip-Gram Model for Network Representation Learning

“…Some graph applications like [30] employ graph neural network with PPR to improve information propagation for node classification. Lasagne [31] utilizes PPR to find important neighbors in the large-scale community, and C_PPR [32] is designed for community detection by VOLUME 10, 2022 PPR measurement of node proximity globally. However, few studies properly apply PPR to the Skip-gram model.…”