A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources

Wang, Xiao; Bo, Deyu; Shi, Chuan; Fan, Shaohua; Ye, Yanfang; Yu, Philip S.

doi:10.1109/tbdata.2022.3177455

Cited by 178 publications

(47 citation statements)

References 141 publications

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“…Definition 3. Heterogeneous Graph Embedding [1]. Given a heterogeneous graph G = (V, E, A, R), where nodes with type A ∈ A are associated with the attribute matrix X A ∈ R |V A |×d A , heterogeneous graph embedding aims to obtain the d-dimensional representation h v ∈ R d for v ∈ V, where d |V|.…”

Section: Knowledge Distillationmentioning

confidence: 99%

“…Heterogeneous graphs, which contain various types of nodes and relations, are ubiquitous in the real world, such as academic networks, movie networks, and business networks [1,2]. For example, IMDB in Figure 1(a) contains three types of nodes: actors, movies, and directors, as well as different types of relations between them (see Figure 1(b)).…”

Section: Introductionmentioning

confidence: 99%

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HIRE: Distilling High-order Relational Knowledge From Heterogeneous Graph Neural Networks

Liu¹,

Tongya²,

Hao³

2022

Preprint

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Researchers have recently proposed plenty of heterogeneous graph neural networks (HGNNs) due to the ubiquity of heterogeneous graphs in both academic and industrial areas. Instead of pursuing a more powerful HGNN model, in this paper, we are interested in devising a versatile plug-and-play module, which accounts for distilling relational knowledge from pre-trained HGNNs. To the best of our knowledge, we are the first to propose a HIgh-order RElational (HIRE) knowledge distillation framework on heterogeneous graphs, which can significantly boost the prediction performance regardless of model architectures of HGNNs. Concretely, our HIRE framework initially performs first-order node-level knowledge distillation, which encodes the semantics of the teacher HGNN with its prediction logits. Meanwhile, the second-order relation-level knowledge distillation imitates the relational correlation between node embeddings of different types generated by the teacher HGNN. Extensive experiments on various popular HGNNs models and three real-world heterogeneous graphs demonstrate that our method obtains consistent and considerable performance enhancement, proving its effectiveness and generalization ability.

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Section: Knowledge Distillationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HIRE: Distilling High-order Relational Knowledge From Heterogeneous Graph Neural Networks

Liu¹,

Tongya²,

Hao³

2022

Preprint

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“…Recently, many studies model the data with complex interaction relationships as heterogeneous information networks or heterogeneous graphs, which can comprehensively retain the original semantic and interaction pattern of objects. Heterogeneous information network represents a graph consisting of different types of entities (nodes) or relations (edges), whose definition is given as [26]: Definition 1. Heterogeneous information network (or heterogeneous graph).…”

Section: Heterogeneous Graphmentioning

confidence: 99%

“…To effectively describe the rich semantic information in the heterogeneous information network, metapath is always used to represent the combination of relationships between different types of objects [32][33][34]. The definition of metapath is given as follows [26]: Definition 2 Metapath. Based on a network schema S = ðA, RÞ, we can express a metapath as a sequence of binary relationships between two objects.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Abnormal User Detection via Multiview Graph Clustering in the Mobile e-Commerce Network

Wang

2022

Wireless Communications and Mobile Computing

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In recent years, Internet of Things has not only promoted the continuous development of e-commerce transaction but also brought loop-hole to the fraud gangs who always utilize mobile devices to commit fraud crimes. For example, fraud gangs are usually organized to purchase commodities at low prices in e-commerce promotions. They benefit from the price spread by reselling commodities at high prices. In the past few years, the transaction fraud caused serious financial losses to merchants in e-commerce platform. To detect the fraudulent user and behavior effectively, a multiview graph clustering-based abnormal detection model is developed in this paper. In the proposed model, two fraudulent behavior patterns are proposed by abstracting the e-commerce network as a heterogeneous information graph. On this basis, two user-similarity graphs are reorganized from the heterogeneous graph with the help of different metapaths. Subsequently, in order to capture the corresponding fraudulent behavior patterns, the above two graphs are encoded into user embeddings and assigned to specific clusters in respective views. Finally, the consensus detection result is produced by fusing the complementary information of different views in a joint multiview learning framework. As we know, our work is the first one that uses multiview graph clustering in e-commerce fraud detection, which will provide a new research perspective for fraud detection in e-commerce platform. Extensive experiments are conducted on real and semisynthetic datasets, and the results demonstrate the effectiveness and superiority of the proposed model.

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“…Graph Neural Network (GNN), as a powerful deep representation learning tool to deal with graph data, has drawn increasing attention and is widely applied to node classification (Kipf and Welling 2017;Velickovic et al 2018;Xu et al 2019), graph classification (Duvenaud et al 2015;Lee, Lee, and Kang 2019), and recommendation (Ying et al 2018;Fan et al 2019b;Wang et al 2019a). Recently, with the proliferation of real-world applications on heterogeneous graphs, which consist of multiple types of nodes and links (Shi et al 2017), Heterogeneous Graph Neural Networks (HGNNs) are brought forward and have achieved remarkable improvements on series of applications (Wang et al 2020a;Hu et al 2019b;Li et al 2019;Hu et al 2019a;Fan et al 2019a;Wang et al 2020b).…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Graph Structure Learning for Graph Neural Networks

Zhao

Wang

Shi

et al. 2021

AAAI

Self Cite

190

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Heterogeneous Graph Neural Networks (HGNNs) have drawn increasing attention in recent years and achieved outstanding performance in many tasks. The success of the existing HGNNs relies on one fundamental assumption, i.e., the original heterogeneous graph structure is reliable. However, this assumption is usually unrealistic, since the heterogeneous graph in reality is inevitably noisy or incomplete. Therefore, it is vital to learn the heterogeneous graph structure for HGNNs rather than rely only on the raw graph structure. In light of this, we make the first attempt towards learning an optimal heterogeneous graph structure for HGNNs and propose a novel framework HGSL, which jointly performs Heterogeneous Graph Structure Learning and GNN parameters learning for classification task. Different from traditional GSL on homogeneous graph, considering the heterogeneity of different relations in heterogeneous graph, HGSL generates each relation subgraph independently. Specifically, in each generated relation subgraph, HGSL not only considers the feature similarity by generating feature similarity graph, but also considers the complex heterogeneous interactions in features and semantics by generating feature propagation graph and semantic graph. Then, these graphs are fused to a learned heterogeneous graph and optimized together with a GNN towards classification objective. Extensive experiments on real-world graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods.

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A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources

Cited by 178 publications

References 141 publications

HIRE: Distilling High-order Relational Knowledge From Heterogeneous Graph Neural Networks

HIRE: Distilling High-order Relational Knowledge From Heterogeneous Graph Neural Networks

Abnormal User Detection via Multiview Graph Clustering in the Mobile e-Commerce Network

Heterogeneous Graph Structure Learning for Graph Neural Networks

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