MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning

• URL: http://arxiv.org/abs/2406.02778v3
• Date: Tue, 29 Oct 2024 00:18:40 GMT
• Title: MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning
• Authors: Shay Deutsch, Lionel Yelibi, Alex Tong Lin, Arjun Ravi Kannan,
• Abstract summary: This paper introduces a framework for multi-scale graph network embedding based on spectral graph wavelets. We show that in Paley-Wiener spaces on graphs, the spectral graph wavelets operator provides greater flexibility and control over smoothness. An additional key advantage of the proposed embedding is its ability to establish a correspondence between the embedding and input feature spaces.
• Score: 1.8124328823188354
• License:
• Abstract: Deriving meaningful representations from complex, high-dimensional data in unsupervised settings is crucial across diverse machine learning applications. This paper introduces a framework for multi-scale graph network embedding based on spectral graph wavelets that employs a contrastive learning approach. We theoretically show that in Paley-Wiener spaces on combinatorial graphs, the spectral graph wavelets operator provides greater flexibility and control over smoothness compared to the Laplacian operator, motivating our approach. An additional key advantage of the proposed embedding is its ability to establish a correspondence between the embedding and input feature spaces, enabling the derivation of feature importance. We validate the effectiveness of our graph embedding framework on multiple public datasets across various downstream tasks, including clustering and unsupervised feature importance.

Related papers

• Towards Graph Prompt Learning: A Survey and Beyond [38.55555996765227]
  Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability. This survey categorizes over 100 relevant works in this field, summarizing general design principles and the latest applications.
  arXiv  Detail & Related papers  (2024-08-26T06:36:42Z)
• Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning [53.81365215811222]
  We provide a review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning. We categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. We discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field.
  arXiv  Detail & Related papers  (2024-02-26T07:52:40Z)
• AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference [6.115315198322837]
  We introduce the Attentional Multi-Embedding Selection (AMES) framework, a differentiable method for selecting the best embedding space for latent graph inference. Our framework consistently achieves comparable or superior results compared to previous methods for latent graph inference.
  arXiv  Detail & Related papers  (2023-11-20T16:24:23Z)
• Multi-View Graph Representation Learning Beyond Homophily [2.601278669926709]
  Unsupervised graph representation learning(GRL) aims to distill diverse graph information into task-agnostic embeddings without label supervision. A novel framework, denoted as Multi-view Graph(MVGE) is proposed, and a set of key designs are identified.
  arXiv  Detail & Related papers  (2023-04-15T08:35:49Z)
• Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning [30.74535386745822]
  This paper proposes a joint deep learning framework called Learnable Graph Convolutional Network and Feature Fusion (LGCN-FF) It consists of two stages: feature fusion network and learnable graph convolutional network. The proposed LGCN-FF is validated to be superior to various state-of-the-art methods in multi-view semi-supervised classification.
  arXiv  Detail & Related papers  (2022-11-16T19:07:12Z)
• 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)
• Effective and Efficient Graph Learning for Multi-view Clustering [173.8313827799077]
  We propose an effective and efficient graph learning model for multi-view clustering. Our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size.
  arXiv  Detail & Related papers  (2021-08-15T13:14:28Z)
• Group Contrastive Self-Supervised Learning on Graphs [101.45974132613293]
  We study self-supervised learning on graphs using contrastive methods. We argue that contrasting graphs in multiple subspaces enables graph encoders to capture more abundant characteristics.
  arXiv  Detail & Related papers  (2021-07-20T22:09:21Z)
• Multiple Graph Learning for Scalable Multi-view Clustering [26.846642220480863]
  We propose an efficient multiple graph learning model via a small number of anchor points and tensor Schatten p-norm minimization. Specifically, we construct a hidden and tractable large graph by anchor graph for each view. We develop an efficient algorithm, which scales linearly with the data size, to solve our proposed model.
  arXiv  Detail & Related papers  (2021-06-29T13:10:56Z)
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning [74.05478502080658]
  This paper introduces a tensor-graph convolutional network (TGCN) for scalable semi-supervised learning (SSL) from data associated with a collection of graphs, that are represented by a tensor. The proposed architecture achieves markedly improved performance relative to standard GCNs, copes with state-of-the-art adversarial attacks, and leads to remarkable SSL performance over protein-to-protein interaction networks.
  arXiv  Detail & Related papers  (2020-03-15T02:33:21Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

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.