Dual-Center Graph Clustering with Neighbor Distribution
Graph clustering is crucial for unraveling intricate data structures, yet it presents significant challenges due to its unsupervised nature. Recently, goal-directed clustering techniques have yielded impressive results, with contrastive learning methods leveraging pseudo-label garnering considerable attention. Nonetheless, pseudo-label as a supervision signal is unreliable and existing goal-directed approaches utilize only features to construct a single-target distribution for single-center optimization, which lead to incomplete and less dependable guidance. In our work, we propose a novel Dual-Center Graph Clustering (DCGC) approach based on neighbor distribution properties, which includes representation learning with neighbor distribution and dual-center optimization. Specifically, we utilize neighbor distribution as a supervision signal to mine hard negative samples in contrastive learning, which is reliable and enhances the effectiveness of representation learning. Furthermore, neighbor distribution center is introduced alongside feature center to jointly construct a dual-target distribution for dual-center optimization. Extensive experiments and analysis demonstrate superior performance and effectiveness of our proposed method.
Code (0)
등록된 구현이 없습니다.
Tasks
Representation LearningContrastive LearningGraph ClusteringSimilar Papers 제목 키워드 기반
Seeing All From a Few: Nodes Selection Using Graph Pooling for Graph Clustering
Recently, there has been considerable research interest in graph clustering aimed at data partition using the graph information. However, one limitation of the most of graph-based methods is that they assume the graph st…
AllClusteringGraph ClusteringGraph Embedding+1Improved Approximation Algorithms for Individually Fair Clustering
We consider the $k$-clustering problem with $\ell_p$-norm cost, which includes $k$-median, $k$-means and $k$-center, under an individual notion of fairness proposed by Jung et al. [2020]: given a set of points $P$ of siz…
ClusteringFairnessA Center in Your Neighborhood: Fairness in Facility Location
When selecting locations for a set of facilities, standard clustering algorithms may place unfair burden on some individuals and neighborhoods. We formulate a fairness concept that takes local population densities into a…
ClusteringFairnessCenter-Oriented Prototype Contrastive Clustering
Contrastive learning is widely used in clustering tasks due to its discriminative representation. However, the conflict problem between classes is difficult to solve effectively. Existing methods try to solve this proble…
Contrastive LearningSCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering
Time series anomaly detection plays a crucial role in a wide range of real-world applications. Reconstruction-based methods have become the mainstream paradigm, but they suffer from over-generalization and under-generali…
Time Series Anomaly Detection