Heterogeneous Graph Neural Network

Zhang, Chuxu; Song, Dongjin; Huang, Chao; Swami, Ananthram; Chawla, Nitesh V.

doi:10.1145/3292500.3330961

Cited by 1,120 publications

(511 citation statements)

References 28 publications

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“…We use the implementation provided in PyG. • Heterogeneous Graph Neural Networks (HetGNN) [27], which adopts different Bi-LSTMs for different node type for aggregating neighbor information. We re-implement this model in PyG following the authors' official code.…”

Section: Methodsmentioning

confidence: 99%

“…This module simply conducts different linear projections for nodes of different types. Such a procedure can be regarded to map heterogeneous data into the same distribution, which is also adopted in literature [23,27]. Implementation Details.…”

Section: Methodsmentioning

confidence: 99%

“…All GNNs keep 3 layers so that the receptive fields of each network are exactly ‡ Unless other stated, HGT refers to HGT +RT E +H e t e r . GNN Models GCN [9] RGCN [14] GAT [22] HetGNN [27] the same. All baselines are optimized via the AdamW optimizer [13] with the Cosine Annealing Learning Rate Scheduler [12].…”

Section: Methodsmentioning

confidence: 99%

“…RGCN keeps a distinct linear projection weight for each edge type. Zhang et al [27] present the heterogeneous graph neural networks (HetGNN) that adopts different RNNs for different node types to integrate multi-modal features. Wang et al [23] extend graph attention networks by maintaining different weights for different meta-path-defined edges.…”

Section: Heterogeneous Gnnsmentioning

confidence: 99%

“…One of the classical paradigms is to define and use meta paths to model heterogeneous structures, such as PathSim [18] and metapath2vec [3]. Recently, in view of graph neural networks' (GNNs) success [7,9,22], there are several attempts to adopt GNNs to learn with heterogeneous networks [14,23,26,27]. However, these works face several issues: First, most of them involve the design of meta paths for each type of heterogeneous graphs, requiring specific domain knowledge; Second, they either simply assume that different types of nodes/edges share the same feature and representation space or keep distinct non-sharing weights for either node type or edge type alone, making them insufficient to capture heterogeneous graphs' properties; Third, most of them ignore the dynamic nature of every (heterogeneous) graph; Finally, their intrinsic design and implementation make them incapable of modeling Web-scale heterogeneous graphs.…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneous Graph Transformer

Dong

Wang

et al. 2020

Proceedings of the Web Conference 2020

882

483

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Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all nodes and edges belong to the same types, making them infeasible to represent heterogeneous structures. In this paper, we present the Heterogeneous Graph Transformer (HGT) architecture for modeling Web-scale heterogeneous graphs. To model heterogeneity, we design node-and edge-type dependent parameters to characterize the heterogeneous attention over each edge, empowering HGT to maintain dedicated representations for different types of nodes and edges. To handle dynamic heterogeneous graphs, we introduce the relative temporal encoding technique into HGT, which is able to capture the dynamic structural dependency with arbitrary durations. To handle Web-scale graph data, we design the heterogeneous mini-batch graph sampling algorithm-HGSampling-for efficient and scalable training. Extensive experiments on the Open Academic Graph of 179 million nodes and 2 billion edges show that the proposed HGT model consistently outperforms all the state-of-the-art GNN baselines by 9%-21% on various downstream tasks. The dataset and source code of HGT are publicly available at https://github.com/acbull/pyHGT.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Heterogeneous Gnnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Heterogeneous Graph Transformer

Dong

Wang

et al. 2020

Proceedings of the Web Conference 2020

882

483

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Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation

Zhang

et al. 2023

Asso for Info Science & Tech

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Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter‐/cross‐/multi‐disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion‐based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co‐topic layer and a co‐authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments—one with a local dataset and the other with a global dataset—demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.

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AHNA: Adaptive representation learning for attributed heterogeneous networks

Lin

Chen

Xing

et al. 2021

Int J of Intelligent Sys

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Meta‐path‐based random walk strategy has attracted tremendous attention in heterogeneous network representation, which can capture network semantics with heterogeneous neighborhoods of nodes. Despite the success of meta‐path‐based random walk strategy in plain heterogeneous networks which contain no attributes, it remains unexplored how meta‐path‐based random walk strategy could be utilized on attributed heterogeneous networks to simultaneously capture structural heterogeneity and attribute proximity. Moreover, the importance of node attributes and structural relations generally varies across data sets, thus requiring careful considerations when they are incorporated into representations. To tackle these problems, we propose a novel method, Attributed Heterogeneous Network embedding based on Aggregate‐path (AHNA), which generates aggregate‐path‐based random walks on attributed heterogeneous networks and adaptively fuses topological structures and node attributes based on the learned importance. Specifically, AHNA first converts node attributes to additional links in the network to deal with the heterogeneity of structures and attributes, which is followed by an adaptive random walk strategy to strike the importance balance between node attributes and topological structures, thereby generating high‐quality representations. Extensive experiments are conducted on three real‐world data sets, where AHNA outperforms state‐of‐the‐art approaches by up to 22.7%, 2.6%, and 2.3% on link prediction, community detection, and node classification, respectively. Moreover, our qualitative analysis indicates that AHNA can capture different balances of topological structures and node attributes on various data sets and thus boost the quality of node representations.

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Heterogeneous Graph Neural Network

Cited by 1,120 publications

References 28 publications

Heterogeneous Graph Transformer

Heterogeneous Graph Transformer

Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation

AHNA: Adaptive representation learning for attributed heterogeneous networks

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