ArticleProceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining2022
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations.
Article in Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of molecular representation learning foundation models.Briefings in bioinformatics · 2026Pooled it
- MMPCS: multi-view molecular pretraining based on consistency information and specific information.Bioinformatics (Oxford, England) · 2026Article
- Prototype based contrastive graph clustering network for reducing false negatives.Scientific reports · 2025Article
- A data augmentation model integrating supervised and unsupervised learning for recommendation.Scientific reports · 2025Article
- Detecting anomalous proteins using deep representations.NAR genomics and bioinformatics · 2024Article
- A Good View for Graph Contrastive Learning.Entropy (Basel, Switzerland) · 2024Article
- GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization.BMC genomics · 2023Article
- Graph Domain Adaptation via Theory-Grounded Spectral Regularization.... International Conference on Learning Representations · 2023Article
- Cross-modality and self-supervised protein embedding for compound-protein affinity and contact prediction.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Self-supervision is recently surging at its new frontier of graph learning. It facilitates graph representations beneficial to downstream tasks; but its success could hinge on domain knowledge for handcraft or the often expensive trials and errors. Even its state-of-the-art representative, graph contrastive learning (GraphCL), is not completely free of those needs as GraphCL uses a prefabricated prior reflected by the ad-hoc manual selection of graph data augmentations. Our work aims at advancing GraphCL by answering the following questions:
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.