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Found 9 papers, 6 papers with code
Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning
Given that the right to be forgotten should be upheld for every individual, we advocate for a more rigorous evaluation of LLM unlearning methods.
LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation
By interpreting the partial order of nodes as a sequence of bipartite graphs, LayerDAG leverages autoregressive generation to model directional dependencies and employs diffusion models to capture logical dependencies within each bipartite graph.
Simple is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
However, current KG-based RAG frameworks still struggle to optimize the trade-off between retrieval effectiveness and efficiency in identifying a suitable amount of relevant graph information for the LLM to digest.
KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web.
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
However, these models face challenges in generating large attributed graphs due to the complex attribute-structure correlations and the large size of these graphs.
Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces
In this work, we benchmark a variety of single- and multi-task graph neural network (GNN) models against lower-bar and higher-bar traditional machine learning approaches employing human engineered molecular features.
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science
Graph neural networks (GNNs) constitute a class of deep learning methods for graph data.
Characterize and Transfer Attention in Graph Neural Networks
Does attention matter and, if so, when and how?
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.
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