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Graph Contrastive Learning with Adaptive Augmentation
[article]
2020
arXiv
pre-print
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In this paper, we propose a novel graph contrastive representation learning method with adaptive augmentation that incorporates various priors for topological and semantic aspects of the graph. ...
framework with adaptive augmentation. ...
To this end, we propose a novel contrastive framework for unsupervised graph representation learning (as shown in Figure 1 ), which we refer to as Graph Contrastive learning with Adaptive augmentation ...
arXiv:2010.14945v1
fatcat:grkjpwzitfelbmwfqev4vni4zq
Fairness-Aware Node Representation Learning
[article]
2021
arXiv
pre-print
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To this end, this study addresses fairness issues in graph contrastive learning with fairness-aware graph augmentation designs, through adaptive feature masking and edge deletion. ...
Despite the success of graph contrastive learning and consequent growing interest, fairness is largely under-explored in the field. ...
Wang, “Graph contrastive learning with adaptive
augmentation,” in Proc. Web Conference (WWW), April 2021.
[18] C. Agarwal, H. Lakkaraju*, and M. ...
arXiv:2106.05391v1
fatcat:gbzvlecaeresdjn52ec3osvdwe
Graph Data Augmentation for Graph Machine Learning: A Survey
[article]
2022
arXiv
pre-print
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We provide a GitHub repository (https://github.com/zhao-tong/graph-data-augmentation-papers) with a reading list that will be continuously updated. ...
Next, we introduce recent advances in graph data augmentation, separating by their learning objectives and methodologies. ...
Contrastive Learning In the past few years, with the rapid development of contrastive learning, several graph contrastive learning methods [You et al., 2021] have been proposed. ...
arXiv:2202.08871v1
fatcat:gjf7mgihkfbqdg6cqscflcw6ga
Hyperspectral Image Classification With Contrastive Graph Convolutional Network
[article]
2022
arXiv
pre-print
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In addition, an adaptive graph augmentation technique is designed to flexibly incorporate the spectral-spatial priors of HSI, which helps facilitate the subsequent contrastive representation learning. ...
To enhance the feature representation ability, in this paper, a GCN model with contrastive learning is proposed to explore the supervision signals contained in both spectral information and spatial relations ...
[30] developed graph contrastive learning with four types of graph data augmentations, each of which imposed certain prior on graph data and encoded the extent and pattern. ...
arXiv:2205.11237v1
fatcat:3ih5erimlfdxlh46ldrhrrfe74
Multi-Level Graph Contrastive Learning
[article]
2021
arXiv
pre-print
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In this paper, we propose a Multi-Level Graph Contrastive Learning (MLGCL) framework for learning robust representation of graph data by contrasting space views of graphs. ...
Extensive experiments indicate MLGCL achieves promising results compared with the existing state-of-the-art graph representation learning methods on seven datasets. ...
GPT [12] ), contrastive learning coupled with GNNs graph representation learning becomes an open task. ...
arXiv:2107.02639v1
fatcat:c7zsvxgiczcj7f5evasefn4yue
Spatio-Temporal Graph Contrastive Learning
[article]
2021
arXiv
pre-print
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To alleviate these limitations, an intuitive idea is to use the popular data augmentation and contrastive learning techniques. ...
In this paper, we propose a Spatio-Temporal Graph Contrastive Learning framework (STGCL) to tackle these issues. ...
Graph Contrastive Learning framework (STGCL). ...
arXiv:2108.11873v1
fatcat:ktkpm3m33fdr5lxglcmqcijpce
Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection
[article]
2022
arXiv
pre-print
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These object instance relations are modelled using an Instance Relation Graph (IRG) network, which are then used to guide the contrastive representation learning. ...
More precisely, we design a novel contrastive loss to enhance the target representations by exploiting the objects relations for a given target domain input. ...
Conclusion In this work, we presented a novel approach for source-free domain adaptive detection using graph-guided contrastive learning. ...
arXiv:2203.15793v2
fatcat:lwijjg6zmfc2hmah3yb6q7udj4
Neural Graph Matching for Pre-training Graph Neural Networks
[article]
2022
arXiv
pre-print
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In this way, we can learn adaptive representations for a given graph when paired with different graphs, and both node- and graph-level characteristics are naturally considered in a single pre-training ...
Focusing on a pair of graphs, we propose to learn structural correspondences between them via neural graph matching, consisting of both intra-graph message passing and inter-graph message passing. ...
Contrastive Learning with Adaptive Graph Representations Contrastive learning is a commonly used technique to learn with augmented graph views in pairs [28] . ...
arXiv:2203.01597v1
fatcat:fq6zrtbbcvdd5dgcxp2cgwgazy
Graph Contrastive Learning Automated
[article]
2021
arXiv
pre-print
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Among many, graph contrastive learning (GraphCL) has emerged with promising representation learning performance. ...
Self-supervised learning on graph-structured data has drawn recent interest for learning generalizable, transferable and robust representations from unlabeled graphs. ...
by JOAO, we design a new augmentation-aware projection head for graph contrastive learning. ...
arXiv:2106.07594v2
fatcat:kkgtxgxwqjfnfhoqnmg4bsjqu4
Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph Learning
[article]
2021
arXiv
pre-print
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This paper proposes an efficient yet effective end-to-end framework, namely Contrastive Adaptive Propagation Graph Neural Networks (CAPGNN), to address these issues by combining Personalized PageRank and ...
In addition, we leverage self-supervised learning techniques and design a negative-free entropy-aware contrastive loss to explicitly take advantage of unlabeled data for training. ...
Our contrastive loss constrains our GNN model to learn consistent semantics for the same vertex across multiple randomly augmented views of a graph. ...
arXiv:2112.01110v1
fatcat:yaviuqnj3zdmddeo6i3rhz5vb4
Adversarial Graph Contrastive Learning with Information Regularization
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2202.06491v2 fatcat:xn5jjmn7mfezdblfn6jmomqliiOther Versions
Contrastive learning is an effective unsupervised method in graph representation learning. Recently, the data augmentation based contrastive learning method has been extended from images to graphs. ...
Unlike the data augmentation on images, the data augmentation on graphs is far less intuitive and much harder to provide high-quality contrastive samples, which are the key to the performance of contrastive ...
Graph Contrastive learning with Adaptive augmentation (GCA) [41] introduces an adaptive data augmentation method which perturbs both the node features and edges according to their importance, and it ...
arXiv:2202.06491v3
fatcat:dbunnvzl6zeyfdfshhivhjynbi
An Empirical Study of Graph Contrastive Learning
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2109.01116v1 fatcat:qjnqzhdhujbs3fs6llppsjvwv4
Graph Contrastive Learning (GCL) establishes a new paradigm for learning graph representations without human annotations. ...
Our empirical studies suggest a set of general receipts for effective GCL, e.g., simple topology augmentations that produce sparse graph views bring promising performance improvements; contrasting modes ...
Graph Contrastive Learning (GCL) adapts the idea of CL to the graph domain. ...
arXiv:2109.01116v2
fatcat:kjfrkg26tbfxhoiomx2mdgao5y
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction
[article]
2021
arXiv
pre-print
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Though graph contrastive learning (GCL) methods have achieved extraordinary performance with insufficient labeled data, most focused on designing data augmentation schemes for general graphs. ...
However, the fundamental property of a molecule could be altered with the augmentation method (like random perturbation) on molecular graphs. ...
to learn the representations of both graphs adaptively. ...
arXiv:2109.11730v1
fatcat:l2chcnrs45bexehlgvry6a2mhm
Graph Self-Supervised Learning: A Survey
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2103.00111v3 fatcat:6ujifqbtbnafvfoht4c6apak7mOther Versions
Deep learning on graphs has attracted significant interests recently. ...
Under the umbrella of graph self-supervised learning, we present a timely and comprehensive review of the existing approaches which employ SSL techniques for graph data. ...
In 2021, more advanced techniques are integrated with graph SSL, such as adaptive augmentation (GCA [69] ), automatic machine learning (JOAO [70] ), and adversarial augmentation (AD-GCL [75] ). when ...
arXiv:2103.00111v4
fatcat:y3zfg4ennnbnhhvmujd5rvltty
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation
[article]
2022
arXiv
pre-print
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We also introduce a graph contrastive augmentation strategy to extract informative graph knowledge in a self-supervised manner. ...
Finally, we design a joint objective function of recommendation and contrastive learning to optimize the model. ...
Graph Contrastive Augmentation Since the above objective function only considers explicit and supervised information in the URI-Graph, we further introduce a graph contrastive augmentation strategy to ...
arXiv:2205.14005v1
fatcat:s2zvy7ohhfeklhf72u4mxhz4q4
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