Spectral Subspace Clustering for Attributed Graphs
- URL: http://arxiv.org/abs/2411.11074v1
- Date: Sun, 17 Nov 2024 13:22:15 GMT
- Title: Spectral Subspace Clustering for Attributed Graphs
- Authors: Xiaoyang Lin, Renchi Yang, Haoran Zheng, Xiangyu Ke,
- Abstract summary: Subspace clustering seeks to identify subspaces that segment a set of n data points into k (kn) groups.
This paper presents two effective and efficient algorithms, S2CAG and M-S2CAG, for SCAG computation.
- Score: 3.974852803981998
- License:
- Abstract: Subspace clustering seeks to identify subspaces that segment a set of n data points into k (k<<n) groups, which has emerged as a powerful tool for analyzing data from various domains, especially images and videos. Recently, several studies have demonstrated the great potential of subspace clustering models for partitioning vertices in attributed graphs, referred to as SCAG. However, these works either demand significant computational overhead for constructing the nxn self-expressive matrix, or fail to incorporate graph topology and attribute data into the subspace clustering framework effectively, and thus, compromise result quality. Motivated by this, this paper presents two effective and efficient algorithms, S2CAG and M-S2CAG, for SCAG computation. Particularly, S2CAG obtains superb performance through three major contributions. First, we formulate a new objective function for SCAG with a refined representation model for vertices and two non-trivial constraints. On top of that, an efficient linear-time optimization solver is developed based on our theoretically grounded problem transformation and well-thought-out adaptive strategy. We then conduct an in-depth analysis to disclose the theoretical connection of S2CAG to conductance minimization, which further inspires the design of M-S2CAG that maximizes the modularity. Our extensive experiments, comparing S2CAG and M-S2CAG against 17 competitors over 8 benchmark datasets, exhibit that our solutions outperform all baselines in terms of clustering quality measured against the ground truth while delivering high efficiency
Related papers
- Effective Clustering on Large Attributed Bipartite Graphs [10.701751248623863]
Attributed bipartite graphs (ABGs) are an expressive data model for describing the interactions between two sets of heterogeneous nodes.
Partitioning the target node set in such graphs into k disjoint clusters (referred to as k-ABGC) finds widespread use in various domains.
However, the majority of existing solutions towards k-ABGC either overlook attribute information or fail to capture bipartite graph structures accurately.
We propose TPO, an effective and efficient approach to k-ABGC that achieves superb clustering performance on multiple real datasets.
arXiv Detail & Related papers (2024-05-20T09:58:27Z) - Efficient High-Quality Clustering for Large Bipartite Graphs [7.533043289759316]
k-Bipartite Graph Clustering (k-BGC) is to partition the target vertices set in a bipartite graph into k disjoint clusters.
The clustering quality is important to the utility of k-BGC in various applications like social network analysis, recommendation systems, text mining, and bioinformatics.
This paper presents two efficient k-BGC solutions, HOPE and HOPE+, which achieve state-of-the-art performance on large-scale bipartite graphs.
arXiv Detail & Related papers (2023-12-28T09:50:56Z) - HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel
Neural Architecture Search [104.45426861115972]
We propose to directly generate structural parameters by utilizing the specifically designed hyper kernels.
We obtain three kinds of networks to separately conduct pixel-level or image-level classifications with 1-D or 3-D convolutions.
A series of experiments on six public datasets demonstrate that the proposed methods achieve state-of-the-art results.
arXiv Detail & Related papers (2023-04-23T17:27:40Z) - Graph Spectral Embedding using the Geodesic Betweeness Centrality [76.27138343125985]
We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph representation of local similarity, connectivity, and global structure.
GSE uses the solution of the Sylvester equation to capture both network structure and neighborhood proximity in a single representation.
arXiv Detail & Related papers (2022-05-07T04:11:23Z) - Graph-based hierarchical record clustering for unsupervised entity
resolution [0.0]
We build upon a state-of-the-art probabilistic framework named the Data Washing Machine (DWM)
We introduce a graph-based hierarchical 2-step record clustering method (GDWM) that first identifies large, connected components or soft clusters in the matched record pairs.
That is followed by breaking down the discovered soft clusters into more precise entity clusters in a hierarchical manner.
arXiv Detail & Related papers (2021-12-12T21:58:07Z) - Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image [88.60285937702304]
This paper proposes a novel unsupervised approach called spatial-spectral clustering with anchor graph (SSCAG) for HSI data clustering.
The proposed SSCAG is competitive against the state-of-the-art approaches.
arXiv Detail & Related papers (2021-04-24T08:09:27Z) - Self-supervised Geometric Perception [96.89966337518854]
Self-supervised geometric perception is a framework to learn a feature descriptor for correspondence matching without any ground-truth geometric model labels.
We show that SGP achieves state-of-the-art performance that is on-par or superior to the supervised oracles trained using ground-truth labels.
arXiv Detail & Related papers (2021-03-04T15:34:43Z) - Graph Convolutional Subspace Clustering: A Robust Subspace Clustering
Framework for Hyperspectral Image [6.332208511335129]
We present a novel subspace clustering framework called Graph Convolutional Subspace Clustering (GCSC) for robust HSI clustering.
Specifically, the framework recasts the self-expressiveness property of the data into the non-Euclidean domain.
We show that traditional subspace clustering models are the special forms of our framework with the Euclidean data.
arXiv Detail & Related papers (2020-04-22T10:09:19Z) - Learning to Cluster Faces via Confidence and Connectivity Estimation [136.5291151775236]
We propose a fully learnable clustering framework without requiring a large number of overlapped subgraphs.
Our method significantly improves clustering accuracy and thus performance of the recognition models trained on top, yet it is an order of magnitude more efficient than existing supervised methods.
arXiv Detail & Related papers (2020-04-01T13:39:37Z) - Embedding Graph Auto-Encoder for Graph Clustering [90.8576971748142]
Graph auto-encoder (GAE) models are based on semi-supervised graph convolution networks (GCN)
We design a specific GAE-based model for graph clustering to be consistent with the theory, namely Embedding Graph Auto-Encoder (EGAE)
EGAE consists of one encoder and dual decoders.
arXiv Detail & Related papers (2020-02-20T09:53:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.