Rethinking Semi-Supervised Node Classification with Self-Supervised Graph Clustering

• URL: http://arxiv.org/abs/2511.19976v1
• Date: Tue, 25 Nov 2025 06:45:13 GMT
• Title: Rethinking Semi-Supervised Node Classification with Self-Supervised Graph Clustering
• Authors: Songbo Wang, Renchi Yang, Yurui Lai, Xiaoyang Lin, Tsz Nam Chan,
• Abstract summary: In real graphs, nodes tend to form tightly-knit communities/clusters, which embody abundant signals for compensating label scarcity.<n>This paper presents NCGC that integrates self-supervised graph clustering and semi-supervised classification into a unified framework.<n>Our experiments showcase that the proposed NCGC framework consistently and considerably outperforms popular GNN models.
• Score: 16.281945909563486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of graph neural networks (GNNs) has offered a powerful tool for semi-supervised node classification tasks. Subsequent studies have achieved further improvements through refining the message passing schemes in GNN models or exploiting various data augmentation techniques to mitigate limited supervision. In real graphs, nodes often tend to form tightly-knit communities/clusters, which embody abundant signals for compensating label scarcity in semi-supervised node classification but are not explored in prior methods. Inspired by this, this paper presents NCGC that integrates self-supervised graph clustering and semi-supervised classification into a unified framework. Firstly, we theoretically unify the optimization objectives of GNNs and spectral graph clustering, and based on that, develop soft orthogonal GNNs (SOGNs) that leverage a refined message passing paradigm to generate node representations for both classification and clustering. On top of that, NCGC includes a self-supervised graph clustering module that enables the training of SOGNs for learning representations of unlabeled nodes in a self-supervised manner. Particularly, this component comprises two non-trivial clustering objectives and a Sinkhorn-Knopp normalization that transforms predicted cluster assignments into balanced soft pseudo-labels. Through combining the foregoing clustering module with the classification model using a multi-task objective containing the supervised classification loss on labeled data and self-supervised clustering loss on unlabeled data, NCGC promotes synergy between them and achieves enhanced model capacity. Our extensive experiments showcase that the proposed NCGC framework consistently and considerably outperforms popular GNN models and recent baselines for semi-supervised node classification on seven real graphs, when working with various classic GNN backbones.

Related papers

• Deep Cut-informed Graph Embedding and Clustering [36.17182061654739]
  We propose an innovative and non-GNN-based Deep Cut-informed Graph embedding and Clustering framework, namely DCGC.<n>For the encoding module, we derive a cut-informed graph embedding objective to fuse graph structure and attributes by minimizing their joint normalized cut.<n>For the clustering module, we utilize the optimal transport theory to obtain the clustering assignments.
  arXiv  Detail & Related papers  (2025-03-09T14:24:09Z)
• Rethinking Independent Cross-Entropy Loss For Graph-Structured Data [41.92169850308025]
  Graph neural networks (GNNs) have exhibited prominent performance in learning graph-structured data. In this work, we propose a new framework, termed joint-cluster supervised learning, to model the joint distribution of each node with its corresponding cluster. In this way, the data-label reference signals extracted from the local cluster explicitly strengthen the discrimination ability on the target node.
  arXiv  Detail & Related papers  (2024-05-24T13:52:41Z)
• Deep Contrastive Graph Learning with Clustering-Oriented Guidance [61.103996105756394]
  Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. Models estimate an initial graph beforehand to apply GCN. Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering.
  arXiv  Detail & Related papers  (2024-02-25T07:03:37Z)
• Mixed Graph Contrastive Network for Semi-Supervised Node Classification [63.924129159538076]
  We propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN)<n>In our method, we improve the discriminative capability of the latent embeddings by an unperturbed augmentation strategy and a correlation reduction mechanism.<n>By combining the two settings, we extract rich supervision information from both the abundant nodes and the rare yet valuable labeled nodes for discriminative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• A Variational Edge Partition Model for Supervised Graph Representation Learning [51.30365677476971]
  This paper introduces a graph generative process to model how the observed edges are generated by aggregating the node interactions over a set of overlapping node communities. We partition each edge into the summation of multiple community-specific weighted edges and use them to define community-specific GNNs. A variational inference framework is proposed to jointly learn a GNN based inference network that partitions the edges into different communities, these community-specific GNNs, and a GNN based predictor that combines community-specific GNNs for the end classification task.
  arXiv  Detail & Related papers  (2022-02-07T14:37:50Z)
• Self-supervised Contrastive Attributed Graph Clustering [110.52694943592974]
  We propose a novel attributed graph clustering network, namely Self-supervised Contrastive Attributed Graph Clustering (SCAGC) In SCAGC, by leveraging inaccurate clustering labels, a self-supervised contrastive loss, are designed for node representation learning. For the OOS nodes, SCAGC can directly calculate their clustering labels.
  arXiv  Detail & Related papers  (2021-10-15T03:25:28Z)
• Attention-driven Graph Clustering Network [49.040136530379094]
  We propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN) AGCN exploits a heterogeneous-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature. AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion.
  arXiv  Detail & Related papers  (2021-08-12T02:30:38Z)
• Learning Hierarchical Graph Neural Networks for Image Clustering [81.5841862489509]
  We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities. Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level.
  arXiv  Detail & Related papers  (2021-07-03T01:28:42Z)
• CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning [19.432449825536423]
  Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision. We present a novel cluster-aware graph neural network (CAGNN) model for unsupervised graph representation learning using self-supervised techniques.
  arXiv  Detail & Related papers  (2020-09-03T13:57:18Z)
• A Collective Learning Framework to Boost GNN Expressiveness [25.394456460032625]
  We consider the task of inductive node classification using Graph Neural Networks (GNNs) in supervised and semi-supervised settings. We propose a general collective learning approach to increase the representation power of any existing GNN. We evaluate performance on five real-world network datasets and demonstrate consistent, significant improvement in node classification accuracy.
  arXiv  Detail & Related papers  (2020-03-26T22:07:28Z)

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.