Directed hypergraph neural network

• URL: http://arxiv.org/abs/2008.03626v3
• Date: Sat, 3 Sep 2022 10:08:58 GMT
• Title: Directed hypergraph neural network
• Authors: Loc Hoang Tran, Linh Hoang Tran
• Abstract summary: We will present the novel neural network method for directed hypergraph. The two datasets that are used in the experiments are the cora and the citeseer datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To deal with irregular data structure, graph convolution neural networks have been developed by a lot of data scientists. However, data scientists just have concentrated primarily on developing deep neural network method for un-directed graph. In this paper, we will present the novel neural network method for directed hypergraph. In the other words, we will develop not only the novel directed hypergraph neural network method but also the novel directed hypergraph based semi-supervised learning method. These methods are employed to solve the node classification task. The two datasets that are used in the experiments are the cora and the citeseer datasets. Among the classic directed graph based semi-supervised learning method, the novel directed hypergraph based semi-supervised learning method, the novel directed hypergraph neural network method that are utilized to solve this node classification task, we recognize that the novel directed hypergraph neural network achieves the highest accuracies.

Related papers

• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection [51.608147732998994]
  Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. We propose a new graph neural network architecture that substitutes classical message passing with an analysis of the local distribution of node features.
  arXiv  Detail & Related papers  (2024-01-17T13:04:23Z)
• Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks [22.564818600608838]
  We introduce a novel prototype-enhanced hypergraph learning approach for node classification in Heterogeneous Information Networks. Our method captures higher-order relationships among nodes and extracts semantic information without relying on metapaths.
  arXiv  Detail & Related papers  (2023-09-22T09:51:15Z)
• A Survey on Graph Classification and Link Prediction based on GNN [11.614366568937761]
  This review article delves into the world of graph convolutional neural networks. It elaborates on the fundamentals of graph convolutional neural networks. It elucidates the graph neural network models based on attention mechanisms and autoencoders.
  arXiv  Detail & Related papers  (2023-07-03T09:08:01Z)
• From Hypergraph Energy Functions to Hypergraph Neural Networks [94.88564151540459]
  We present an expressive family of parameterized, hypergraph-regularized energy functions. We then demonstrate how minimizers of these energies effectively serve as node embeddings. We draw parallels between the proposed bilevel hypergraph optimization, and existing GNN architectures in common use.
  arXiv  Detail & Related papers  (2023-06-16T04:40:59Z)
• Neural Graph Matching for Pre-training Graph Neural Networks [72.32801428070749]
  Graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. We present a novel Graph Matching based GNN Pre-Training framework, called GMPT. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training.
  arXiv  Detail & Related papers  (2022-03-03T09:53:53Z)
• Noise-robust classification with hypergraph neural network [4.003697389752555]
  This paper presents a novel version of the hypergraph neural network method. The accuracies of these five methods are evaluated and compared. Experimental results show that the hypergraph neural network methods achieve the best performance when the noise level increases.
  arXiv  Detail & Related papers  (2021-02-03T08:34:53Z)
• Deep Hypergraph U-Net for Brain Graph Embedding and Classification [0.0]
  Network neuroscience examines the brain as a system represented by a network (or connectome) We propose Hypergraph U-Net, a novel data embedding framework leveraging the hypergraph structure to learn low-dimensional embeddings of data samples. We tested our method on small-scale and large-scale heterogeneous brain connectomic datasets including morphological and functional brain networks of autistic and demented patients.
  arXiv  Detail & Related papers  (2020-08-30T08:15:18Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)
• Graph Structure of Neural Networks [104.33754950606298]
  We show how the graph structure of neural networks affect their predictive performance. A "sweet spot" of relational graphs leads to neural networks with significantly improved predictive performance. Top-performing neural networks have graph structure surprisingly similar to those of real biological neural networks.
  arXiv  Detail & Related papers  (2020-07-13T17:59:31Z)
• Hcore-Init: Neural Network Initialization based on Graph Degeneracy [22.923756039561194]
  We propose an adapted version of the k-core structure for the complete weighted multipartite graph extracted from a deep learning architecture. As a multipartite graph is a combination of bipartite graphs, that are in turn the incidence graphs of hypergraphs, we design k-hypercore decomposition.
  arXiv  Detail & Related papers  (2020-04-16T12:57:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.