Bootstrapped Representation Learning on Graphs

• URL: http://arxiv.org/abs/2102.06514v1
• Date: Fri, 12 Feb 2021 13:36:39 GMT
• Title: Bootstrapped Representation Learning on Graphs
• Authors: Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, R\'emi Munos, Petar Veli\v{c}kovi\'c, Michal Valko
• Abstract summary: Current state-of-the-art self-supervised learning methods for graph neural networks (GNNs) are based on contrastive learning. Inspired by BYOL, we present Bootstrapped Graph Latents, BGRL, a self-supervised graph representation method. BGRL outperforms or matches the previous unsupervised state-of-the-art results on several established benchmark datasets.
• Score: 37.62546075583656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current state-of-the-art self-supervised learning methods for graph neural networks (GNNs) are based on contrastive learning. As such, they heavily depend on the construction of augmentations and negative examples. For example, on the standard PPI benchmark, increasing the number of negative pairs improves performance, thereby requiring computation and memory cost quadratic in the number of nodes to achieve peak performance. Inspired by BYOL, a recently introduced method for self-supervised learning that does not require negative pairs, we present Bootstrapped Graph Latents, BGRL, a self-supervised graph representation method that gets rid of this potentially quadratic bottleneck. BGRL outperforms or matches the previous unsupervised state-of-the-art results on several established benchmark datasets. Moreover, it enables the effective usage of graph attentional (GAT) encoders, allowing us to further improve the state of the art. In particular on the PPI dataset, using GAT as an encoder we achieve state-of-the-art 70.49% Micro-F1, using the linear evaluation protocol. On all other datasets under consideration, our model is competitive with the equivalent supervised GNN results, often exceeding them.

Related papers

• Faster Inference Time for GNNs using coarsening [1.323700980948722]
  coarsening-based methods are used to reduce the graph into a smaller one, resulting in faster computation. No previous research has tackled the cost during the inference. This paper presents a novel approach to improve the scalability of GNNs through subgraph-based techniques.
  arXiv  Detail & Related papers  (2024-10-19T06:27:24Z)
• Amplify Graph Learning for Recommendation via Sparsity Completion [16.32861024767423]
  Graph learning models have been widely deployed in collaborative filtering (CF) based recommendation systems. Due to the issue of data sparsity, the graph structure of the original input lacks potential positive preference edges. We propose an Amplify Graph Learning framework based on Sparsity Completion (called AGL-SC)
  arXiv  Detail & Related papers  (2024-06-27T08:26:20Z)
• GOODAT: Towards Test-time Graph Out-of-Distribution Detection [103.40396427724667]
  Graph neural networks (GNNs) have found widespread application in modeling graph data across diverse domains. Recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN. This paper introduces a data-centric, unsupervised, and plug-and-play solution that operates independently of training data and modifications of GNN architecture.
  arXiv  Detail & Related papers  (2024-01-10T08:37:39Z)
• Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification [25.831508778029097]
  We introduce AMUD, which quantifies the relationship between node profiles and topology from a statistical perspective. We also propose ADPA as a new directed graph learning paradigm for AMUD.
  arXiv  Detail & Related papers  (2023-12-07T07:54:11Z)
• From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks [0.0]
  Graph Convolutional Networks (GCNs) have been shown to be a powerful concept that has been successfully applied to a large variety of tasks. We study the theory that paved the way to the definition of GCN, including related parts of classical graph theory.
  arXiv  Detail & Related papers  (2022-07-12T16:57:08Z)
• Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training [82.68805025636165]
  We propose to select positive graph instances directly from existing graphs in the training set. Our selection is based on certain domain-specific pair-wise similarity measurements. Besides, we develop an adaptive node-level pre-training method to dynamically mask nodes to distribute them evenly in the graph.
  arXiv  Detail & Related papers  (2022-06-23T20:12:51Z)
• Benchmarking Node Outlier Detection on Graphs [90.29966986023403]
  Graph outlier detection is an emerging but crucial machine learning task with numerous applications. We present the first comprehensive unsupervised node outlier detection benchmark for graphs called UNOD.
  arXiv  Detail & Related papers  (2022-06-21T01:46:38Z)
• Robust Optimization as Data Augmentation for Large-scale Graphs [117.2376815614148]
  We propose FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training. FLAG is a general-purpose approach for graph data, which universally works in node classification, link prediction, and graph classification tasks.
  arXiv  Detail & Related papers  (2020-10-19T21:51:47Z)
• Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification [70.50751397870972]
  FastGAT is a method to make attention based GNNs lightweight by using spectral sparsification to generate an optimal pruning of the input graph. We experimentally evaluate FastGAT on several large real world graph datasets for node classification tasks.
  arXiv  Detail & Related papers  (2020-06-15T22:07:54Z)
• Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College [80.67842220664231]
  We propose a novel pre-processing technique, namely ELectoral COllege (ELCO), which automatically expands new nodes and edges to refine the label similarity within a dense subgraph. In all setups tested, our method boosts the average score of base models by a large margin of 4.7 points, as well as consistently outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2020-06-10T14:48:48Z)

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.