MoCL: Contrastive Learning on Molecular Graphs with Multi-level Domain Knowledge

• URL: http://arxiv.org/abs/2106.04509v1
• Date: Sat, 5 Jun 2021 18:00:51 GMT
• Title: MoCL: Contrastive Learning on Molecular Graphs with Multi-level Domain Knowledge
• Authors: Mengying Sun, Jing Xing, Huijun Wang, Bin Chen, Jiayu Zhou
• Abstract summary: We propose a novel framework called MoCL, which utilizes domain knowledge at both local- and global-level to assist representation learning. We evaluate MoCL on various molecular datasets under both linear and semi-supervised settings.
• Score: 28.386302970315736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent years have seen a rapid growth of utilizing graph neural networks (GNNs) in the biomedical domain for tackling drug-related problems. However, like any other deep architectures, GNNs are data hungry. While requiring labels in real world is often expensive, pretraining GNNs in an unsupervised manner has been actively explored. Among them, graph contrastive learning, by maximizing the mutual information between paired graph augmentations, has been shown to be effective on various downstream tasks. However, the current graph contrastive learning framework has two limitations. First, the augmentations are designed for general graphs and thus may not be suitable or powerful enough for certain domains. Second, the contrastive scheme only learns representations that are invariant to local perturbations and thus does not consider the global structure of the dataset, which may also be useful for downstream tasks. Therefore, in this paper, we study graph contrastive learning in the context of biomedical domain, where molecular graphs are present. We propose a novel framework called MoCL, which utilizes domain knowledge at both local- and global-level to assist representation learning. The local-level domain knowledge guides the augmentation process such that variation is introduced without changing graph semantics. The global-level knowledge encodes the similarity information between graphs in the entire dataset and helps to learn representations with richer semantics. The entire model is learned through a double contrast objective. We evaluate MoCL on various molecular datasets under both linear and semi-supervised settings and results show that MoCL achieves state-of-the-art performance.

Related papers

• GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning [0.0]
  Graph representation learning has emerged as a powerful tool for preserving graph topology when mapping nodes to vector representations. Current graph neural network models face the challenge of requiring extensive labeled data. We propose Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning.
  arXiv  Detail & Related papers  (2024-09-12T03:09:05Z)
• TGNN: A Joint Semi-supervised Framework for Graph-level Classification [34.300070497510276]
  We propose a novel semi-supervised framework called Twin Graph Neural Network (TGNN) To explore graph structural information from complementary views, our TGNN has a message passing module and a graph kernel module. We evaluate our TGNN on various public datasets and show that it achieves strong performance.
  arXiv  Detail & Related papers  (2023-04-23T15:42:11Z)
• 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)
• Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions [51.597480162777074]
  Graph neural networks (GNNs) rely on the message passing paradigm to propagate node features and build interactions. Recent works point out that different graph learning tasks require different ranges of interactions between nodes. We study two common graph construction methods in scientific domains, i.e., emphK-nearest neighbor (KNN) graphs and emphfully-connected (FC) graphs.
  arXiv  Detail & Related papers  (2022-05-15T11:38:14Z)
• Graph Structure Learning with Variational Information Bottleneck [70.62851953251253]
  We propose a novel Variational Information Bottleneck guided Graph Structure Learning framework, namely VIB-GSL. VIB-GSL learns an informative and compressive graph structure to distill the actionable information for specific downstream tasks.
  arXiv  Detail & Related papers  (2021-12-16T14:22:13Z)
• Graph Representation Learning via Contrasting Cluster Assignments [57.87743170674533]
  We propose a novel unsupervised graph representation model by contrasting cluster assignments, called as GRCCA. It is motivated to make good use of local and global information synthetically through combining clustering algorithms and contrastive learning. GRCCA has strong competitiveness in most tasks.
  arXiv  Detail & Related papers  (2021-12-15T07:28:58Z)
• Multi-Level Graph Contrastive Learning [38.022118893733804]
  We propose a Multi-Level Graph Contrastive Learning (MLGCL) framework for learning robust representation of graph data by contrasting space views of graphs. The original graph is first-order approximation structure and contains uncertainty or error, while the $k$NN graph generated by encoding features preserves high-order proximity. Extensive experiments indicate MLGCL achieves promising results compared with the existing state-of-the-art graph representation learning methods on seven datasets.
  arXiv  Detail & Related papers  (2021-07-06T14:24:43Z)
• Graph Contrastive Learning with Augmentations [109.23158429991298]
  We propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data. We show that our framework can produce graph representations of similar or better generalizability, transferrability, and robustness compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-10-22T20:13:43Z)
• Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning [21.0019144298605]
  Existing graph neural networks fed with the complete graph data are not scalable due to limited computation and memory costs. textscSubg-Con is proposed by utilizing the strong correlation between central nodes and their sampled subgraphs to capture regional structure information. Compared with existing graph representation learning approaches, textscSubg-Con has prominent performance advantages in weaker supervision requirements, model learning scalability, and parallelization.
  arXiv  Detail & Related papers  (2020-09-22T01:58:19Z)
• Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification [63.56018768401328]
  We propose a Multi-level Graph Convolutional Network (GCN) with Automatic Graph Learning method (MGCN-AGL) for HSI classification. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions. Our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level.
  arXiv  Detail & Related papers  (2020-09-19T09:26:20Z)
• Differentiable Graph Module (DGM) for Graph Convolutional Networks [44.26665239213658]
  Differentiable Graph Module (DGM) is a learnable function that predicts edge probabilities in the graph which are optimal for the downstream task. We provide an extensive evaluation of applications from the domains of healthcare (disease prediction), brain imaging (age prediction), computer graphics (3D point cloud segmentation), and computer vision (zero-shot learning)
  arXiv  Detail & Related papers  (2020-02-11T12:59:35Z)

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.