Built independently by an author, for readers. Read the story and support ChapterPal
ChapterPal Logo
Log inSign up

keyword

high-order random walks

High-order random walks are stochastic graph traversal processes that model multi-step transition dynamics and multi-hop reachability to capture broader structural relationships beyond immediate neighborhood connections. Unlike standard first-order random walks that depend solely on direct transitions between adjacent nodes, high-order random walks incorporate longer sequence paths, powers of transition probability matrices, or higher-order relational structures such as network motifs and hypergraphs. By accumulating transition probabilities across extended paths, this approach models diffusion dynamics and structural similarities across a global topology. Consequently, high-order random walks enable network analysis and graph machine learning models to discover latent, long-range dependencies, distinguish meaningful affinities from local connectivity noise, and effectively characterize complex community structures.

1 item

Decoupled Contrastive Multi-View Clustering with High-Order Random Walks

Decoupled Contrastive Multi-View Clustering with High-Order Random Walks

Yiding Lu, Yijie Lin, Mouxing Yang, Dezhong Peng, Peng Hu, Xi Peng

OrganizationsSichuan University

Why you should read this

Proposes a decoupled multi-view clustering framework that uses high-order random walks to rectify false positive and false negative pairs globally while preserving view-specific information through cross-view reconstruction.

In recent, some robust contrastive multi-view clustering (MvC) methods have been proposed, which construct data pairs from neighborhoods to alleviate the false negative issue, i.e., some intra-cluster samples are wrongly treated as negative pairs. Although promising performance has been achieved by these methods, the false negative issue is still far from addressed and the false positive issue emerges because all in- and out-of-neighborhood samples are simply treated as positive and negative, respectively. To address the issues, we propose a novel robust method, dubbed decoupled contrastive multi-view clustering with high-order random walks (DIVIDE). In brief, DIVIDE leverages random walks to progressively identify data pairs in a global instead of local manner. As a result, DIVIDE could identify in-neighborhood negatives and out-of-neighborhood positives. Moreover, DIVIDE embraces a novel MvC architecture to perform inter- and intra-view contrastive learning in different embedding spaces, thus boosting clustering performance and embracing the robustness against missing views. To verify the efficacy of DIVIDE, we carry out extensive experiments on four benchmark datasets comparing with nine state-of-the-art MvC methods in both complete and incomplete MvC settings. The code is released on https://github.com/XLearning-SCU/2024-AAAI-DIVIDE.

Added

2026-09-26