(arXiv 2023.10) Graph Neural Architecture Search with GPT-4 [paper] (arXiv 2023.11) Biomedical knowledge graph-enhanced prompt generation for large language models [paper][code] (arXiv 2023.11) Graph-Guided Rea
The ArangoDB-PyG Adapter exports Graphs from ArangoDB, the multi-model database for graph & beyond, into PyTorch Geometric (PyG), a PyTorch-based Graph Neural Network library, and vice-versa. On July 29 2022, we introduced the first release of the PyTorch Geometric Adapter to the ArangoML...
This review explores the role of Graph Neural Networks (GNNs) in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection. Specifically, by examining a series of detailed research questions, this review delves into the...
Instruction Design.The instruction for our graph matching task consists of three components: i) graph information, ii) human ques-tion, and iii) GraphGPT response. In this task, we treat each node in the graph as a central node and perform h-hops with random neighbor sampling, resulting in...
Visual navigation needs the agent locate the given target with visual perception. To enable robots to effectively execute tasks, combining large language m
To address the intricate challenges posed by the spatial and temporal distribution of crash data, we introduce the Spatio-temporal Zero-Inflated Tweedie Graph Neural Network (STZITD-GNN). This model combines spatio-temporal graph machine learning with statistical methods, enabling enhanced uncertainty qu...
and then fed the calculated contact maps into a graph convolutional neural network. GraphIdn achieved accuracy values of 88.51% and 89.93% in independent testing and fivefold cross-validation, respectively, outperforming previous state-of-the-art predictors. As far as we know, this is the first ...
However, existing approaches either overlook the inherent permutation symmetry in the neural network or rely on intricate weight-sharing patterns to achieve equivariance, while ignoring the impact of the network architecture itself. In this work, we propose to represent neural networks as computational ...
Keywordsmulti-behaviormodeling;sequentialrecommendation;graphneuralnetwork;MLParchitecture;global itemgraph 摘要在多行为序列推荐领域,图神经网络(GNNs)虽被广泛应用,但存在局限性,如对序列间协同信号建模不足和处理长距离依赖性等问题.针对这些问题,提出了一种新的解决框架GraphMLP-Mixer.该框架首先构造全局物品图来增强...
Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground...