We further undertake a comparative analysis of hypergraph neural networks and GNNs from both the spectral and the spatial perspectives, represented by the HGNN and HGNN+, respectively. From the spectral perspec
Notably, we pioneer the application of hypergraph neural networks within the realm of learning-based optimization for general combinatorial optimization problems with higher order constraints. • Enabling scalability to much larger problems by introducing a new distributed and parallel architecture for ...
The Hyper-SAGNN Architecture for Hypergraph Representation Learning The detailed description of this part of the method can be found in our recent work Zhang et al. (2020). The structure of the neural network for Hyper-SAGNN is shown in Figure 2B. The input to the model can be represented ...
The learning architecture of CHESHIRE has four major steps: feature initialization, feature refinement, pooling, and scoring (Fig.1e, f). For feature initialization, we employ an encoder-based one-layer neural network31to generate a feature vector for each metabolite from the incidence matrix (see...
Hypergraph Neural Networks (HGNNs) introduce hypergraph structures, enabling the more effective modeling of complex relationships and high-order interactions, thereby enhancing the accuracy and personalization of recommendation systems. However, most graph- or hypergraph-based methods fail to account for ...
The spectral analysis methods dominated and most of the research was based on spectral theory before neural networks were introduced to the field of graph representation learning. With the emergence of various deep learning algorithms, researchers have also started to try to extend some deep learning...
Thus, it seems promising to take the theory and all the basic concepts of deep learning and neural nets, rip out the explicit tensor-algebra in those theories, and replace them by sheaves. A crude sketch is here. Some primitive, basic infrastructure has been built. Huge remaining work items...
The learning architecture of CHESHIRE has four major steps: feature initialization, feature refinement, pooling, and scoring (Fig. 1e, f). For feature initialization, we employ an encoder-based one-layer neural network31 to generate a feature vector for each metabolite from the incidence matrix ...
Most traffic prediction methods employ GNNs to extract spatial features and aggregate these using Recurrent Neural Networks (RNNs) or Temporal Convolutional Networks (TCNs). However, these methods are unable to fully model the high-order complexities inherent in traffic data. Thus, uncovering and ...
change detection tasks, including the scarcity and dispersion of labeled samples, the difficulty in efficiently extracting features from unstructured image objects, and the underutilization of high-order correlation information, we propose a novel architecture based on hypergraph convolutional neural networks....