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   Articles with "graph contrastive" as a keyword



Heterogeneous graph contrastive learning with gradient balance for drug repositioning

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recommendations! Published in 2024 at "Briefings in Bioinformatics"

DOI: 10.1093/bib/bbae650

Abstract: Abstract Drug repositioning, which involves identifying new therapeutic indications for approved drugs, is pivotal in accelerating drug discovery. Recently, to mitigate the effect of label sparsity on inferring potential drug–disease associations (DDAs), graph contrastive learning… read more here.

Keywords: heterogeneous graph; drug repositioning; graph contrastive; gradient balance ... See more keywords

Similarity measures based graph co-contrastive learning for drug-disease association prediction.

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recommendations! Published in 2023 at "Bioinformatics"

DOI: 10.1093/bioinformatics/btad357

Abstract: MOTIVATION An imperative step in drug discovery is the prediction of drug-disease associations (DDAs), which tries to uncover potential therapeutic possibilities for already validated drugs. It is costly and time-consuming to predict DDAs using wet… read more here.

Keywords: graph contrastive; drug disease; prediction; contrastive learning ... See more keywords

Seeking False Hard Negatives for Graph Contrastive Learning

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recommendations! Published in 2024 at "IEEE Transactions on Circuits and Systems for Video Technology"

DOI: 10.1109/tcsvt.2024.3370149

Abstract: Graph Contrastive Learning (GCL) has achieved great success in self-supervised representation learning throughout positive and negative pairs based on graph neural networks (GNNs), where one critical issue lies in how to handle the false hard… read more here.

Keywords: graph contrastive; contrastive learning; order; hard negatives ... See more keywords

Superpixel Graph Contrastive Clustering With Semantic-Invariant Augmentations for Hyperspectral Images

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recommendations! Published in 2024 at "IEEE Transactions on Circuits and Systems for Video Technology"

DOI: 10.1109/tcsvt.2024.3418610

Abstract: Hyperspectral images (HSI) clustering is an important but challenging task. The state-of-the-art (SOTA) methods usually rely on superpixels, however, they do not fully utilize the spatial and spectral information in HSI 3-D structure, and their… read more here.

Keywords: superpixel graph; graph contrastive; semantic invariant; contrastive clustering ... See more keywords

MCTGCL: Mixed CNN–Transformer for Mars Hyperspectral Image Classification With Graph Contrastive Learning

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recommendations! Published in 2025 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2025.3529996

Abstract: Hyperspectral image (HSI) classification has been extensively studied in the context of Earth observation. However, its application in Mars exploration remains limited. Although convolutional neural networks (CNNs) have proven effective in HSI processing, their local… read more here.

Keywords: classification; graph contrastive; cnn transformer; contrastive learning ... See more keywords

Toward Thermal Infrared Image Colorization via Large Kernel Convolution and Patch-Wise Graph Contrastive Learning

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recommendations! Published in 2025 at "IEEE Transactions on Intelligent Transportation Systems"

DOI: 10.1109/tits.2025.3629031

Abstract: In the context of intelligent transportation systems (ITS), accurate thermal infrared image colorization plays a vital role in improving visual interpretation of traffic scenes, facilitating downstream analysis of vehicles and pedestrians under complex environmental conditions.… read more here.

Keywords: infrared image; image; thermal infrared; graph contrastive ... See more keywords

Towards Effective and Robust Graph Contrastive Learning With Graph Autoencoding

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recommendations! Published in 2024 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2023.3288280

Abstract: Graph contrastive learning (GCL) has become the de-facto approach to conducting self-supervised learning on graphs for its superior performance. However, non-semantic graph augmentation methods prevent it from achieving better performance, and it suffers from vulnerability… read more here.

Keywords: graph contrastive; towards effective; topology; contrastive learning ... See more keywords

Interdependence-Adaptive Mutual Information Maximization for Graph Contrastive Learning

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recommendations! Published in 2024 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2024.3423409

Abstract: Despite remarkable advancements in graph contrastive learning techniques, the identification of interdependent relationships when maximizing cross-view mutual information remains a challenging issue, primarily due to the complexity of graph topology. In this study, we propose… read more here.

Keywords: contrastive learning; information; graph contrastive; interdependence ... See more keywords

Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing

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recommendations! Published in 2024 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2024.3425891

Abstract: Searching on bipartite graphs serves as a fundamental task for various real-world applications, such as recommendation systems, database retrieval, and document querying. Conventional approaches rely on similarity matching in continuous euclidean space of vectorized node… read more here.

Keywords: search; graph contrastive; effective top; bipartite graph ... See more keywords

Simplified Graph Contrastive Learning Model Without Augmentation

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recommendations! Published in 2025 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2025.3590482

Abstract: Burgeoning graph contrastive learning (GCL) stands out in the graph domain with low annotated costs and high model performance improvements, which is typically composed of three standard configurations: 1) graph data augmentation (GraphDA), 2) multi-branch… read more here.

Keywords: multi branch; graph contrastive; augmentation; model ... See more keywords

Graph Contrastive Learning for Fusion of Graph Structure and Attribute Information

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recommendations! Published in 2025 at "IEEE Transactions on Multimedia"

DOI: 10.1109/tmm.2025.3542984

Abstract: Graph Contrastive Learning (GCL) plays a crucial role in multimedia applications due to its effectiveness in analyzing graph-structured data. Existing GCL methods focus on maximizing the agreement of node representations across different augmentations, which leads… read more here.

Keywords: information; fusion; graph contrastive; graph structure ... See more keywords