Multiplex Heterogeneous Graph Convolutional Network

• URL: http://arxiv.org/abs/2208.06129v1
• Date: Fri, 12 Aug 2022 06:17:54 GMT
• Title: Multiplex Heterogeneous Graph Convolutional Network
• Authors: Pengyang Yu, Chaofan Fu, Yanwei Yu, Chao Huang, Zhongying Zhao, Junyu Dong
• Abstract summary: This work proposes a Multiplex Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous network embedding. Our MHGCN can automatically learn the useful heterogeneous meta-path interactions of different lengths in multiplex heterogeneous networks.
• Score: 25.494590588212542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex network between multi-typed nodes and different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a Multiplex Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous network embedding. Our MHGCN can automatically learn the useful heterogeneous meta-path interactions of different lengths in multiplex heterogeneous networks through multi-layer convolution aggregation. Additionally, we effectively integrate both multi-relation structural signals and attribute semantics into the learned node embeddings with both unsupervised and semi-supervised learning paradigms. Extensive experiments on five real-world datasets with various network analytical tasks demonstrate the significant superiority of MHGCN against state-of-the-art embedding baselines in terms of all evaluation metrics.

Related papers

• Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs [29.618952407794783]
  We propose anAttributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors. AMOGCN gains superior semi-supervised classification performance compared with state-of-the-art competitors.
  arXiv  Detail & Related papers  (2023-04-13T08:31:16Z)
• Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph [58.99478502486377]
  We propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections.
  arXiv  Detail & Related papers  (2022-09-23T05:24:18Z)
• HybridGNN: Learning Hybrid Representation in Multiplex Heterogeneous Networks [26.549559266395775]
  We propose HybridGNN, an end-to-end graph neural network model with hybrid aggregation flows and hierarchical attentions. We show that HybridGNN achieves the best performance compared to several state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-08-03T13:39:47Z)
• Simple and Efficient Heterogeneous Graph Neural Network [55.56564522532328]
  Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) over homogeneous graphs, especially the attention mechanism and the multi-layer structure. This paper conducts an in-depth and detailed study of these mechanisms and proposes Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN)
  arXiv  Detail & Related papers  (2022-07-06T10:01:46Z)
• Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks [0.0]
  RAHMeN is a novel unified relation-aware embedding framework for attributed heterogeneous multiplex networks. Our model incorporates node attributes, motif-based features, relation-based GCN approaches, and relational self-attention to learn embeddings of nodes. We evaluate our model on four real-world datasets from Amazon, Twitter, YouTube, and Tissue PPIs in both transductive and inductive settings.
  arXiv  Detail & Related papers  (2022-03-03T18:31:29Z)
• Heterogeneous Graph Neural Network with Multi-view Representation Learning [16.31723570596291]
  We propose a Heterogeneous Graph Neural Network with Multi-View Representation Learning (MV-HetGNN) for heterogeneous graph embedding. The proposed model consists of node feature transformation, view-specific ego graph encoding and auto multi-view fusion to thoroughly learn complex structural and semantic information for generating comprehensive node representations. Extensive experiments on three real-world heterogeneous graph datasets show that the proposed MV-HetGNN model consistently outperforms all the state-of-the-art GNN baselines in various downstream tasks.
  arXiv  Detail & Related papers  (2021-08-31T07:18:48Z)
• Attention-driven Graph Clustering Network [49.040136530379094]
  We propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN) AGCN exploits a heterogeneous-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature. AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion.
  arXiv  Detail & Related papers  (2021-08-12T02:30:38Z)
• Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks [68.9026534589483]
  RioGNN is a novel Reinforced, recursive and flexible neighborhood selection guided multi-relational Graph Neural Network architecture. RioGNN can learn more discriminative node embedding with enhanced explainability due to the recognition of individual importance of each relation.
  arXiv  Detail & Related papers  (2021-04-16T04:30:06Z)
• Layer-stacked Attention for Heterogeneous Network Embedding [0.0]
  Layer-stacked ATTention Embedding (LATTE) is an architecture that automatically decomposes higher-order meta relations at each layer. LATTE offers a more interpretable aggregation scheme for nodes of different types at different neighborhood ranges. In both transductive and inductive node classification tasks, LATTE can achieve state-of-the-art performance compared to existing approaches.
  arXiv  Detail & Related papers  (2020-09-17T05:13:41Z)
• A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning [52.83948119677194]
  We propose a multi-semantic metapath (MSM) model for large scale heterogeneous representation learning. Specifically, we generate multi-semantic metapath-based random walks to construct the heterogeneous neighborhood to handle the unbalanced distributions. We conduct systematical evaluations for the proposed framework on two challenging datasets: Amazon and Alibaba.
  arXiv  Detail & Related papers  (2020-07-19T22:50:20Z)
• Detecting Communities in Heterogeneous Multi-Relational Networks:A Message Passing based Approach [89.19237792558687]
  Community is a common characteristic of networks including social networks, biological networks, computer and information networks. We propose an efficient message passing based algorithm to simultaneously detect communities for all homogeneous networks.
  arXiv  Detail & Related papers  (2020-04-06T17:36:24Z)

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.