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Found 9 papers, 4 papers with code
MolBind: Multimodal Alignment of Language, Molecules, and Proteins
Recent advancements in biology and chemistry have leveraged multi-modal learning, integrating molecules and their natural language descriptions to enhance drug discovery.
3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of Molecular Graphs
However, practical applications call for methods that generate diverse, and ideally novel, molecules with the desired properties.
Simple and Asymmetric Graph Contrastive Learning without Augmentations
Experimental results show that the simple GraphACL significantly outperforms state-of-the-art graph contrastive learning and self-supervised learning methods on homophilic and heterophilic graphs.
On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused?
A natural question is "could alignment really prevent those open-sourced large language models from being misused to generate undesired content?''.
Fairness-aware Message Passing for Graph Neural Networks
Graph Neural Networks (GNNs) have shown great power in various domains.
Self-Explainable Graph Neural Networks for Link Prediction
Directly adopting existing post-hoc explainers for explaining link prediction is sub-optimal because: (i) post-hoc explainers usually adopt another strategy or model to explain a target model, which could misinterpret the target model; and (ii) GNN explainers for node classification identify crucial subgraphs around each node for the explanation; while for link prediction, one needs to explain the prediction for each pair of nodes based on graph structure and node attributes.
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society.
Learning Fair Models without Sensitive Attributes: A Generative Approach
The lack of sensitive attributes challenges many existing works.
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks
Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection.
