In the domain of dynamic graph representation learning (DGRL), the efficient and comprehensive capture of temporal evolution within real-world networks is crucial. Spiking Neural Networks (SNNs), known as their temporal dynamics and low-power characteristic, offer an efficient solution for temporal ...
5 Dynamic Graph Representation Learning Via Self-Attention Networks link:https://arxiv.org/abs/1812.09430 Abstract 提出了在动态图上使用自注意力 Conclusion 本文提出了使用自注意力的网络结构用于在动态图学习节点表示。具体地说,DySAT使用(1)结构邻居和(2)历史节点表示上的自我注意来计算动态节点表示,虽然实验...
Graph representation learning techniques can be broadly divided into two categories: (i) static graph embedding, which represents each node in the graph with a single vector; and (ii) dynamic graph embedding, which considers multiple snapshots of a graph and obtains a time series of vectors for...
Awesome papers (codes) about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs) and their applications (i.e. recommendation, knowledge graph completion). GitHub Link: github.com/SpaceLearner Survey Representation Learning for Dynamic Graphs: A Survey (JMLR, ...
在GraphPage中使用不同的聚合器进行实验,即GCN、平均池、最大池和LSTM,以报告每个数据集中性能最好的聚合器的性能。为了与GAT进行公平比较,GAT最初只对节点分类进行实验,论文在GraphSAGE中实现了一个图形注意层作为额外的聚合器,用GraphSAGE+GAT表示。本文还将GCN和GAT训练为自动编码器,用于沿着(Modeling polypharmacy ...
6 dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning207 link:https://scholar.google.com.hk/scholar_url?url=https://arxiv.org/pdf/1809.02657&hl=zh-TW&sa=X&ei=bSGfYviOJOOEywThnbSYCQ&scisig=AAGBfm0bzwUuDvjnCXStu1Abuajctfd1xw&oi=scholarr DTDG Abstract ...
(RNNs), making them suffer seriously from computation and memory overheads on large temporal graphs. So far, scalability of dynamic graph representation learning on large temporal graphs remains one of the major challenges. In this paper, we present a scalable framework, namely SpikeNet, to ...
This graph is dynamically learned with the constraint of similarity, sparseness and semantic correlation. Upon this, DyBGR exchanges the sample (node) information on the batch-graph to update each node representation. Note that, both batch-graph learning and information propagation are jointly ...
MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning (SIGKDD, 2024) TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning (IPDPS, 2024) [paper][code] Mayfly: a Neural Data Structure for Graph Stream Summarization (ICLR, 2024, Spotlight...
3 所以本文提出了Dyngraph2vec,使用多个非线性层来学习每个网络中的结构模式。此外,它利用循环层来学习网络中的时间转换。循环层中的回顾参数控制学习到的时间模式的长度。 本文的4点贡献 1)提出了动态图嵌入模型dyngraph2vec,该模型捕捉时间动态。 2)证明了捕获网络动态可以显著提高链路预测的性能。 3)将展示模型...