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InfoMax Inc deep graph infomax (dgi) model
Deep Graph Infomax (Dgi) Model, 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/graph+model/deep+graph+infomax/pmc11696235-17-9-7
Average 90 stars, based on 1 article reviews
deep graph infomax (dgi) model - by Bioz Stars, 2026-10
90/100 stars

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Related Articles

other:

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: 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: CellNEST reveals cell-cell relay networks using attention mechanisms on spatial transcriptomics.
Article Snippet: As this model requires ground-truth data for supervised model training, we propose using a contrastive learning approach, Deep Graph Infomax (DGI)33, which excels in unsupervised learning problems.

Article Title: SGCLMD: Signed graph-based contrastive learning model for predicting somatic mutation-drug association.
Article Snippet: Somatic mutations could influence critical cellular processes, leading to uncontrolled cell growth and tumor formation.. Understanding the intricate interactions between somatic mutations and drugs was crucial for advancing our knowledge of the underlying biological mechanisms of cancer.. This knowledge, in turn, could drive advancements in cancer detection, diagnosis, and treatment.

Article Title: Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies
Article Snippet: Proust addresses the challenge of distinguishing spots from similar spatial domains by implementing contrastive self-supervised learning (CSL) adapted from Deep Graph Infomax ( ) to learn more discriminative reconstructed biological features ( ).

Gene Expression:

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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