Control-based Graph Embeddings with Data Augmentation for Contrastive Learning

• URL: http://arxiv.org/abs/2403.04923v2
• Date: Thu, 18 Apr 2024 00:10:49 GMT
• Title: Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
• Authors: Obaid Ullah Ahmad, Anwar Said, Mudassir Shabbir, Waseem Abbas, Xenofon Koutsoukos,
• Abstract summary: We study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. A crucial step in contrastive learning is the creation of 'augmented' graphs from the input graphs. Here, we propose a unique method for generating these augmented graphs by leveraging the control properties of networks.
• Score: 3.250579305400297
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. Our approach introduces a novel framework for contrastive learning, a widely prevalent technique for unsupervised representation learning. A crucial step in contrastive learning is the creation of 'augmented' graphs from the input graphs. Though different from the original graphs, these augmented graphs retain the original graph's structural characteristics. Here, we propose a unique method for generating these augmented graphs by leveraging the control properties of networks. The core concept revolves around perturbing the original graph to create a new one while preserving the controllability properties specific to networks and graphs. Compared to the existing methods, we demonstrate that this innovative approach enhances the effectiveness of contrastive learning frameworks, leading to superior results regarding the accuracy of the classification tasks. The key innovation lies in our ability to decode the network structure using these control properties, opening new avenues for unsupervised graph representation learning.

Related papers

• Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification [11.811637154674939]
  This work introduces a novel method for graph stream classification. It operates under the general setting where a data generating process produces graphs with varying nodes and edges over time. It incorporates a loss-based concept drift detection mechanism to recalculate graph prototypes when drift is detected.
  arXiv  Detail & Related papers  (2024-04-03T08:47:32Z)
• Spectral Augmentations for Graph Contrastive Learning [50.149996923976836]
  Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learning for pre-training. We propose a set of well-motivated graph transformation operations to provide a bank of candidates when constructing augmentations for a graph contrastive objective.
  arXiv  Detail & Related papers  (2023-02-06T16:26:29Z)
• State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
  Graph-level learning has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures. Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
  arXiv  Detail & Related papers  (2023-01-14T09:15:49Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• Learning Robust Representation through Graph Adversarial Contrastive Learning [6.332560610460623]
  Existing studies show that node representations generated by graph neural networks (GNNs) are vulnerable to adversarial attacks. We propose a novel Graph Adversarial Contrastive Learning framework (GraphACL) by introducing adversarial augmentations into graph self-supervised learning.
  arXiv  Detail & Related papers  (2022-01-31T07:07:51Z)
• Towards Unsupervised Deep Graph Structure Learning [67.58720734177325]
  We propose an unsupervised graph structure learning paradigm, where the learned graph topology is optimized by data itself without any external guidance. Specifically, we generate a learning target from the original data as an "anchor graph", and use a contrastive loss to maximize the agreement between the anchor graph and the learned graph.
  arXiv  Detail & Related papers  (2022-01-17T11:57:29Z)
• Self-supervised Consensus Representation Learning for Attributed Graph [15.729417511103602]
  We introduce self-supervised learning mechanism to graph representation learning. We propose a novel Self-supervised Consensus Representation Learning framework. Our proposed SCRL method treats graph from two perspectives: topology graph and feature graph.
  arXiv  Detail & Related papers  (2021-08-10T07:53:09Z)
• Graph Representation Learning by Ensemble Aggregating Subgraphs via Mutual Information Maximization [5.419711903307341]
  We introduce a self-supervised learning method to enhance the representations of graph-level learned by Graph Neural Networks. To get a comprehensive understanding of the graph structure, we propose an ensemble-learning like subgraph method. And to achieve efficient and effective contrasive learning, a Head-Tail contrastive samples construction method is proposed.
  arXiv  Detail & Related papers  (2021-03-24T12:06:12Z)
• GraphOpt: Learning Optimization Models of Graph Formation [72.75384705298303]
  We propose an end-to-end framework that learns an implicit model of graph structure formation and discovers an underlying optimization mechanism. The learned objective can serve as an explanation for the observed graph properties, thereby lending itself to transfer across different graphs within a domain. GraphOpt poses link formation in graphs as a sequential decision-making process and solves it using maximum entropy inverse reinforcement learning algorithm.
  arXiv  Detail & Related papers  (2020-07-07T16:51:39Z)
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
  Graph representation learning has emerged as a powerful technique for addressing real-world problems. We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework. We conduct experiments on three graph learning tasks and ten graph datasets.
  arXiv  Detail & Related papers  (2020-06-17T16:18:35Z)

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.