Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation Learning

• URL: http://arxiv.org/abs/2505.23529v2
• Date: Thu, 12 Jun 2025 09:16:52 GMT
• Title: Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation Learning
• Authors: Shifeng Xie, Aref Einizade, Jhony H. Giraldo,
• Abstract summary: Graph Representation Learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors.<n>In this work, we propose a novel Subgraph Gaussian Embedding Contrast (SubGEC) method.
• Score: 2.4305626489408465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Representation Learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. Self-Supervised Learning (SSL) methods are widely used in GRL because they can avoid expensive human annotation. In this work, we propose a novel Subgraph Gaussian Embedding Contrast (SubGEC) method. Our approach introduces a subgraph Gaussian embedding module, which adaptively maps subgraphs to a structured Gaussian space, ensuring the preservation of input subgraph characteristics while generating subgraphs with a controlled distribution. We then employ optimal transport distances, more precisely the Wasserstein and Gromov-Wasserstein distances, to effectively measure the similarity between subgraphs, enhancing the robustness of the contrastive learning process. Extensive experiments across multiple benchmarks demonstrate that \method~outperforms or presents competitive performance against state-of-the-art approaches. Our findings provide insights into the design of SSL methods for GRL, emphasizing the importance of the distribution of the generated contrastive pairs.

Related papers

• SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning [24.649741877466447]
  We propose a novel subgraph-oriented learnable augmentation method for graph contrastive learning, termed SOLA-GCL.<n>SOLA-GCL partitions a graph into multiple densely connected subgraphs based on their intrinsic properties.<n>A graph view generator optimize augmentation strategies for each subgraph, thereby generating tailored views for graph contrastive learning.
  arXiv  Detail & Related papers  (2025-03-13T06:52:39Z)
• Variational Graph Contrastive Learning [1.9950682531209158]
  Graph representation learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. In this work, we propose a novel Subgraph Gaussian Embedding Contrast (SGEC) method.
  arXiv  Detail & Related papers  (2024-11-11T17:23:07Z)
• LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs [22.322402072526927]
  Heterogeneous Graph Contrastive Learning (HGCL) usually requires pre-defined meta-paths. textsfLAMP integrates various meta-path sub-graphs into a unified and stable structure. textsfLAMP significantly outperforms existing state-of-the-art unsupervised models in terms of accuracy and robustness.
  arXiv  Detail & Related papers  (2024-09-10T08:27:39Z)
• Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching [50.30124426442228]
  Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have raised growing concerns. We propose a novel graph method named textbfCraftextbfTing textbfRationatextbf (textbfCTRL) which offers an optimized starting point closer to the original dataset's feature distribution.
  arXiv  Detail & Related papers  (2024-02-07T14:49:10Z)
• Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding [51.75091298017941]
  This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) for attributed graph data. The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets.
  arXiv  Detail & Related papers  (2024-01-12T17:57:07Z)
• Localized Contrastive Learning on Graphs [110.54606263711385]
  We introduce a simple yet effective contrastive model named Localized Graph Contrastive Learning (Local-GCL) In spite of its simplicity, Local-GCL achieves quite competitive performance in self-supervised node representation learning tasks on graphs with various scales and properties.
  arXiv  Detail & Related papers  (2022-12-08T23:36:00Z)
• Generative Subgraph Contrast for Self-Supervised Graph Representation Learning [16.374143635724327]
  We propose a novel adaptive subgraph generation based contrastive learning framework for efficient and robust self-supervised graph representation learning. It aims to generate contrastive samples by capturing the intrinsic structures of the graph and distinguish the samples based on the features and structures of subgraphs simultaneously.
  arXiv  Detail & Related papers  (2022-07-25T09:08:46Z)
• GraphCoCo: Graph Complementary Contrastive Learning [65.89743197355722]
  Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. This paper proposes an effective graph complementary contrastive learning approach named GraphCoCo to tackle the above issue.
  arXiv  Detail & Related papers  (2022-03-24T02:58:36Z)
• Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning [88.60285937702304]
  This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE) Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
  arXiv  Detail & Related papers  (2021-03-25T09:31:41Z)
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning [64.98816284854067]
  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. A novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure.
  arXiv  Detail & Related papers  (2020-09-15T13:59:28Z)
• 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.