由于graph streaming的研究主要focus 在 降低硬件的压力(内存,显存,计算速度等),而不是focus在网络表示的效果的提升上,因此我们在这里不详细调查它们。要更深入地讨论graph streaming,请感兴趣的读者参阅[3]、[32]、[34]。 ([3] C. Aggarwal and K. Subbian, ‘‘Evolutionary network analysis: A survey,’...
Based on this, the paper categorizes dynamic graph neural network link prediction models into two main types according to the granularity of time: discrete dynamic graph models and continuous dynamic graph models, and provides an overview of the modeling methods used...
2.5 Dynamic Network Models 在这一部分文章将动态网络模型根据不同特征分类,具体如下图所示: 其中,random graph models根据已知的结构产生随机连接图;Stochastic Actor Oriented Models(SAOM)将每个节点当做actor考虑并对actor的行动建模;Relational Event Models主要用于交互网络,上面两种都是连续时间模型。 Latent space m...
1 Dynamic Neural Networks: A Survey Yizeng Han , Gao Huang , Shiji Song, Le Yang, Honghui Wang, and Yulin Wang Abstract—Dynamic neural network is an emerging research topic in deep learning. Compared to static models which have fixed computational graphs and parameters at the inference ...
Graph neural networks designed for different graph types: A survey. Trans Mach Learn Res 2023. https://doi.org/10.48550/arXiv.2204.03080 AlBadani B, Shi R, Dong J, Al-Sabri R, Moctard OB. Transformer-based graph convolutional network for sentiment analysis. Appl Sci. 2022;12(3):1316. ...
The graph structures consider incidence dynamic relationships of both inflows and outflows. Then we design a novel dynamic graph recurrent convolutional neural network model, namely Dynamic-GRCNN, to learn the spatial-temporal features representation for urban transportation network topological structures ...
For industrial big data, anomaly detection for multivariate time series data is of critical strategic significance. However, the complexity of industrial e
Recently, there has been increasing interest in building graph neural network models for studying the brain connectome (Bessadok et al., 2021; Isallari and Rekik 2021). Viewing each subject as a node, spectral GCN models have been successfully applied to diagnose Alzheimer's disease and autism ...
图神经网络的系列文章,文章目录如下: 从图(Graph)到图卷积(Graph Convolution):漫谈图神经网络模型 (一) 从图(Graph)到图卷积(Graph Convolution):漫谈图神经网络模型 (二) 从图(Graph)到图卷积(Graph Convolution):漫谈图神经网络模型 (三) 笔者最近看了一些图与图卷积神经网络的论文,深感其强大,但一些Survey...
2. Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey 作者:Joakim Skarding, et al. (University of Technology Sydney) 发表时间:2020.5 发表于:arXiv 标签:动态图表示,综述,动态图神经网络 概述:该文侧重于从图神经网络的角度与具体任务的角度去讲述目前动态网络的...