Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning

• URL: http://arxiv.org/abs/2103.06474v1
• Date: Thu, 11 Mar 2021 05:42:06 GMT
• Title: Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning
• Authors: Bang Lin, Xiuchong Wang, Yu Dong, Chengfu Huo, Weijun Ren, Chuanyu Xu
• Abstract summary: We propose a Metapaths-guided Neighbors-aggregated Heterogeneous Graph Neural Network to improve performance. We conduct extensive experiments for the proposed MHN on three real-world heterogeneous graph datasets.
• Score: 5.228629954007088
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most real-world datasets are inherently heterogeneous graphs, which involve a diversity of node and relation types. Heterogeneous graph embedding is to learn the structure and semantic information from the graph, and then embed it into the low-dimensional node representation. Existing methods usually capture the composite relation of a heterogeneous graph by defining metapath, which represent a semantic of the graph. However, these methods either ignore node attributes, or discard the local and global information of the graph, or only consider one metapath. To address these limitations, we propose a Metapaths-guided Neighbors-aggregated Heterogeneous Graph Neural Network(MHN) to improve performance. Specially, MHN employs node base embedding to encapsulate node attributes, BFS and DFS neighbors aggregation within a metapath to capture local and global information, and metapaths aggregation to combine different semantics of the heterogeneous graph. We conduct extensive experiments for the proposed MHN on three real-world heterogeneous graph datasets, including node classification, link prediction and online A/B test on Alibaba mobile application. Results demonstrate that MHN performs better than other state-of-the-art baselines.

Related papers

• Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph [57.2953563124339]
  We propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN. We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB)
  arXiv  Detail & Related papers  (2023-05-18T07:27:18Z)
• Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions [51.597480162777074]
  Graph neural networks (GNNs) rely on the message passing paradigm to propagate node features and build interactions. Recent works point out that different graph learning tasks require different ranges of interactions between nodes. We study two common graph construction methods in scientific domains, i.e., emphK-nearest neighbor (KNN) graphs and emphfully-connected (FC) graphs.
  arXiv  Detail & Related papers  (2022-05-15T11:38:14Z)
• Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning [102.9138736545956]
  Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies. In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
  arXiv  Detail & Related papers  (2022-04-30T12:57:02Z)
• SHGNN: Structure-Aware Heterogeneous Graph Neural Network [77.78459918119536]
  This paper proposes a novel Structure-Aware Heterogeneous Graph Neural Network (SHGNN) to address the above limitations. We first utilize a feature propagation module to capture the local structure information of intermediate nodes in the meta-path. Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path. Finally, we leverage a meta-path aggregator to fuse the information aggregated from different meta-paths.
  arXiv  Detail & Related papers  (2021-12-12T14:18:18Z)
• HeMI: Multi-view Embedding in Heterogeneous Graphs [8.87527266373087]
  representation learning of heterogeneous graphs (HGs) embeds the rich structure and semantics of such graphs into a low-dimensional space. We propose a self-supervised method that learns HG representations by relying on knowledge exchange and discovery among different HG structural semantics. We show that the proposed self-supervision both outperforms and improves competing methods by 1% and up to 10% for all tasks.
  arXiv  Detail & Related papers  (2021-09-14T23:04:42Z)
• HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning [2.096172374930129]
  We propose a new heterogeneous graph neural network model named HMSG. We decompose the heterogeneous graph into multiple subgraphs. Each subgraph associates specific semantic and structural information. Through a type-specific attribute transformation, node attributes can also be transferred among different types of nodes.
  arXiv  Detail & Related papers  (2021-09-07T05:02:59Z)
• Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks [67.25782890241496]
  We propose a higher-order Attribute-Enhancing Graph Neural Network (HAEGNN) for heterogeneous network representation learning. HAEGNN simultaneously incorporates meta-paths and meta-graphs for rich, heterogeneous semantics. It shows superior performance against the state-of-the-art methods in node classification, node clustering, and visualization.
  arXiv  Detail & Related papers  (2021-04-16T04:56:38Z)
• Learning the Implicit Semantic Representation on Graph-Structured Data [57.670106959061634]
  Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole. We propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs.
  arXiv  Detail & Related papers  (2021-01-16T16:18:43Z)
• MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding [36.6390478350677]
  We propose a new model named Metapath Aggregated Graph Neural Network (MAGNN) to boost the final performance. MAGNN employs three major components, i.e., the node content transformation to encapsulate input node attributes, the intra-metapath aggregation to incorporate intermediate semantic nodes, and the inter-metapath aggregation to combine messages from multiple metapaths. Experiments show that MAGNN achieves more accurate prediction results than state-of-the-art baselines.
  arXiv  Detail & Related papers  (2020-02-05T08:21:00Z)

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.