deep graph infomax (dgi) (InfoMax Inc)

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InfoMax Inc deep graph infomax (dgi)
Deep Graph Infomax (Dgi), supplied by InfoMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deep+graph+infomax+(dgi)/deep+graph+infomax/pm40481363-38-14-19
Average 90 stars, based on 1 article reviews

deep graph infomax (dgi) - by Bioz Stars, 2026-09

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Article Title: BHGNN-RT: Capturing bidirectionality and network heterogeneity in graphs.
Article Snippet: To realize unsupervised clustering, we adopted the objective function inspired by the Deep Graph Infomax (DGI) to maximize the mutual information (MI) between node embeddings and a network summary [44].

Article Title: BHGNN-RT: Capturing bidirectionality and network heterogeneity in graphs
Article Snippet: To realize unsupervised clustering, we adopted the objective function inspired by the Deep Graph Infomax (DGI) to maximize the mutual information (MI) between node embeddings and a network summary [ ].

Article Title: DEANE: Context-Aware Dual-Craft Graph Contrastive Learning for Enhanced Extractive Question Answering
Article Snippet: Notable approaches include Deep Graph Infomax (DGI), which optimizes mutual information between local and global graph representations for node embeddings [27]; GRAph Contrastive rEpresentation learning (GRACE), which leverages random graph augmentations, such as edge and feature removal [28]; and Graph Group Discrimination (GGD), which learns discriminative features by contrasting nodes across both corrupted and uncorrupted versions of the graph [24].

Article Title: SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics
Article Snippet: We also incorporated two contrastive learning-based methods: CCST [ ], based on the Deep Graph Infomax (DGI) approach, and GraphST [ ] which uses DGI while also reconstructing the original features.

Article Title: A rie L: Adversarial Graph Contrastive Learning
Article Snippet: To name a few, Deep Graph Infomax (DGI) [ 51 ] extends Deep InfoMax [ 12 ] and achieves significant improvements over previous random-walk-based methods.

Article Title: SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics.
Article Snippet: We also incorporated two contrastive learning-based methods: CCST [22], based on the Deep Graph Infomax (DGI) approach, and GraphST [23] which uses DGI while also reconstructing the original features.

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Article Title: stGRL: spatial domain identification, denoising, and imputation algorithm for spatial transcriptome data based on multi-task graph contrastive representation learning.
Article Snippet: .. Graph Contrastive Representation Learning Module Unlike the traditional Deep Graph Infomax (DGI) [28] architecture that maximizes the mutual information between the local and the global node representation, this module further improves the model’s sensitivity to contextual node information in gene expression data by designing a contrastive learning task with positive and negative samples. ..

Article Title: stGRL: spatial domain identification, denoising, and imputation algorithm for spatial transcriptome data based on multi-task graph contrastive representation learning
Article Snippet: .. Graph Contrastive Representation Learning Module Unlike the traditional Deep Graph Infomax (DGI) [ ] architecture that maximizes the mutual information between the local and the global node representation, this module further improves the model’s sensitivity to contextual node information in gene expression data by designing a contrastive learning task with positive and negative samples. ..

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