《Deep Learning on Graphs: A Survey》。 Zhang Z, Cui P, Zhu W. Deep learning on graphs: A survey[J]. IEEE Transactions on Knowledge and Data Engineering, 2020. 18年的一篇GNN综述,读完之后,感觉GCN那一部分对我帮助还不小,帮我理清了脉络,也可能是因为之前把《Graph Representation Learning》这本...
图强化学习(Graph reinforcement learning) 图对抗方法(Graph adversarial methods) 这里会主要记录第二部分卷积神经网络的内容。 图卷积网络可以说是最热门的研究方向。论文中的table 4 总结了对比了一些卷积神经网络结构。 卷积操作 谱方法 在上一章学习有关图谱卷积的知识的时候,提到过这部分的内容。对比CNN用卷积核...
Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts ...
Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established ...
Deep Learning on Graphs: A Survey第五章自动编码论文总结 论文地址:https://arxiv.org/pdf/1812.04202.pdf 最近老师让我们读的一片论文,已经开组会讲完了,我负责的是第五章,图的自动编码,现在再总结一遍,便于后者研读。因为这篇论文是一篇综述,所以里边有些符号,在这个模型里是一个意思,在另一个模型了,符号...
翻译:How to do Deep Learning on Graphs with Graph Convolutional Networks 什么是图卷积网络 图卷积网络是一个在图上进行操作的神经网络。给定一个图G=(E,V)G=(E,V),一个GCN的输入包括: 一个输入特征矩阵X,其维度是N×F0N×F0,其中N是节点的数目,F0F0是每个节点输入特征的数目 ...
图强化学习(Graph Reinforcement Learning) 总结与讨论 Zhang Z , Cui P , Zhu W . Deep Learning on Graphs: A Survey[J]. 2018. 深度学习在大量领域表现出明显的效果,无论是语音,图像,还是自然语言处理。但是由于图结构数据具有独特的属性,深度学习并不是自然的适用。最近,在这个方向进行了大量的研究极大地...
Deep learning on graphs with Keras WARNING The documentation for Spektral is still a work in progress and may change substantially before the first proper release. The API is not mature enough to be considered stable, but we'll try to keep breaking changes to a minimum. Drop me an email ...
Morris et al. Future Directions in Foundations of Graph Machine Learning. ICML 2024 Zhao et al. GraphAny: A Foundation Model for Node Classification on Any Graph. Arxiv 2024. Code on Github Dong et al. Universal Link Predicto...
It also includes many self-supervised learning methods on graphs. BTW, we are glad to announce that we will give a tutorial on KDD 2021 in August. Please see this link for more details. 🎉 CogDL supports GNN models with Mixture of Experts (MoE). You can install FastMoE and try MoE ...