Dynamic Hypergraph Structure Learning echooOO 1 人赞同了该文章 Abstract 近年来,超图建模在样本之间的相关性制定方面表现出了优越性,在分类、检索等任务中有着广泛的应用。在所有这些工作中,超图学习的性能高度依赖于生成的超图结构。好的超图结构可以更好地表示数据相关性,反之亦然。为了解决这一问题,本文
Dynamic Hypergraph Structure Learning 摘要 动态超图结构学习 超图构建 结构学习公式 约束 求解 结果 摘要 这篇文章主要解决了超图的构建问题,以往的超图构建都是静态的,这里提出用动态构建,即在学习过程中不断对超图的形态进行构建。这篇论文的亮点我觉得就是使用了标签作为空间特征向量,来辅助对超图节点的聚类。 动态...
In this paper, we propose a multi-modal dynamic hypergraph learning framework for childhood autism diagnosis using both sFCs and dFCs. We collect a childhood ASD dataset including 91 ASD patients and 76 healthy controls (HC). After extracting features from the sFC and dFC for each subject, two...
Hypergraph Dynamic System (ICLR, 2024, Poster) [paper] Deep Temporal Graph Clustering (ICLR, 2024, Poster) [paper][code] GraphPulse: Topological representations for temporal graph property prediction (ICLR, 2024, Poster) [paper][code] Beyond Spatio-Temporal Representations: Evolving Fourier Transform...
Subsequently, we use a dynamic hypergraph network to learn deep features from the transformed graph structure, then constructing relation-aware node representations. Furthermore, we integrate a residual connection to improve the performance of our DRHGNN model. Finally, we design a relationship ...
Dynamic Hypergraph Neural Networks (DHGNN) is a kind of neural networks modeling dynamically evolving hypergraph structures, which is composed of the stacked layers of two modules: dynamic hypergraph construction (DHG) and hypergrpah convolution (HGC). Considering initially constructed hypergraph is prob...
We proposed a novel and effective model named Dual Channel Representation-learning with Dynamic Intent Aggregation (DIA-DCR) to tackle the above problems. Specifically, we used the session graph and the global graph separately as inputs in the dual-channel structure. In the local channel, we imp...
Besides, it can be naturally generalized to hypergraph and graph with edges of different orders. We apply it to four important problems: maximum clique problem, densest k-subgraph problem, structure fitting, and discovery of high-density regions. The extensive experimental results clearly demonstrate ...
Feature hypergraph representation learning on spatial-temporal correlations for EEG emotion recognition. Cogn Neurodyn. 2023;17(5):1271–1281. doi: 10.1007/s11571-022-09890-3. [10] Song T, Zheng W, Song P, et al. EEG emotion recognition using dynam ical graph convolutional neural networks...
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 optimized to boost their respective performance. Furthermore, in practical, DyBGR model can be implemented via a ...