Graph Representation Learning 图表示学习(图神经网络) Graph Representation Learning(Graph Neural Networks, GNN)A Review of methods and applications, Zhou Jie 2020, on AI OpenFigure. An overwiew of computational modules of
Chen, An end-to-end deep learning architecture for graph classification, in: Proceedings of AAAI, 2018. Google Scholar [24] Kipf T.N., Welling M. Variational graph auto-encoders NIPS Workshop on Bayesian Deep Learning (2016) Google Scholar [25] Y. Bai, H. Ding, S. Bian, T. Chen,...
With advancements in deep learning, research on Electroencephalogram (EEG) brain-computer interfaces has progressed significantly, bringing Graph Neural Networks (GNNs)鈥攄esigned for non-Euclidean data鈥攖o the forefront of brain connectivity studies. However, signal-to-noise ratio (SNR) issues in ...
Dimensionality reduction is crucial for the visualization and interpretation of the high-dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving topological structure among cells to low dimensional space remains a challenge. Here, we present the single-cell graph autoencoder (scGAE...
where taking the neighborhood of each pixel into account is critical for the performance of downstream tasks, we introduced a graph convolutional autoencoder that integrates both the gene expression of a cell and that of its neighbors. Our graph-based autoencoder structure decodes both a cell’s...
Code:GitHub - VinciZhu/GiffCF: Official implementation of "Graph Signal Diffusion Model for Collaborative Filtering" (SIGIR 2024) Diffusion 火了好几年了,本文是最先把 diffusion 用作推荐的工作之一(DiffRec之后第二篇?)。 0.摘要 CF 可以看做是”用户反馈数据“的条件生成任务(Conditional generative task...
We introduce a new model for graph-to-text generation, TriELMR (Multi-dimensional Maintains the original structure of Knowledge Graph), which can pay constant attention to the structural information of the graph during the information transformation process, and ultimately generate high-quality text. ...
node2vec: Scalable Feature Learning for Networks Aditya Grover, Jure Leskovec KDD 2016 Breadth-first Search, Depth-first Search, Node Classification, Link Prediction Variational Graph Auto-Encoders Thomas N. Kipf, Max Welling arXiv 1611 Scalable Graph Embedding for Asymmetric Proximity ...
GNNs have attracted a lot of attention over past years and became the standard method for graph learning. Complementary to the abovementioned information, GNNs can be understood as generalised encoder architectures which additionally use the nodes’ features X and the graph structure A as input into...
autoencoder. They integrated human mobility GPS data and historical incident point data at the grid level to map the real-time crash situation in Tokyo. However, while their approach attempted to capture a larger spatial area, it did not consider urban geo-semantic information for precise and ...