Relation Structure-Aware Heterogeneous Information Network Embedding

Lu, Yuanfu; Shi, Chuan; Hu, Linmei; Liu, Zhiyuan

doi:10.1609/aaai.v33i01.33014456

Cited by 111 publications

(46 citation statements)

References 5 publications

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“…RHINE [18] is a heterogeneous information network (HIN) embedding method which using the structural characteristics of heterogeneous relations.…”

Section: Baseline Methodsmentioning

confidence: 99%

“…Heterogeneous network representation learning is a hot topic in current research and has quite good performance in link prediction [18]. Although heterogeneous network representation learning methods have been widely adopted for link prediction of social networks with good results, they have not been used for link prediction of DTIs to be best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Relational Topology-Based Heterogeneous Network Embedding for Predicting Drug-Target Interactions

Ouyang

Liu

et al. 2021

Preprint

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Background: Predicting interactions between drugs and target proteins is a key task in drug discovery. Although the method of validation via wet-lab experiments has become available, experimental methods for drug-target interactions (DTIs) identification remain either time consuming or heavily dependent on domain expertise. Therefore, various computational models have been proposed to predict possible interactions between drugs and target proteins. Usually, we construct a heterogeneous network with drugs and target proteins to calculate the relationship between them. However, most calculation methods do not consider the topological structure of the relationship between drugs and target proteins. Fortunately, Network Embedding Learning provides new and powerful graph analytical approaches for predicting drug-target interaction, which is considering both content and topology of network.Results: In this article, we propose a relational topology-based heterogeneous network embedding method to predict DITs, abbreviated as RTHNE_DTI. We use the ideas of word embeddings to turn heterogeneous network with drugs and target proteins into dense, low-dimensional real-valued vectors. Furthermore, according to two different topological structure of the relationship between the nodes, we represent them separately by training two different models. Then the meaningful vectors represented for drugs and target proteins can be used to calculate the interaction of them easily. Results show that by considering topological structure and different relationship type of drugs and target proteins, RTHNE_DTI outperforms other state-of-the-art methods on both labeled network and unlabeled network.Conclusions: This work proposes heterogeneous network representation learning for DITs prediction. To the best of our knowledge, this study first introduces relation classification to heterogeneous network embedding to improve predicting DTIs efficiently.

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“…RHINE [18] is a heterogeneous information network (HIN) embedding method which using the structural characteristics of heterogeneous relations.…”

Section: Baseline Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Relational Topology-Based Heterogeneous Network Embedding for Predicting Drug-Target Interactions

Ouyang

Liu

et al. 2021

Preprint

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“…• DBLP [Lu et al, 2019]: We extract a subset of DBLP which contains 9556 papers (P), 2000 authors (A), and 20 conferences (C). The authors and papers are divided into four areas: database, data mining, machine learning, and information retrieval.…”

Section: Methodsmentioning

confidence: 99%

“…HIN2Vec [Fu et al, 2017] learns HIN embeddings via predicting different relations in HINs. RHINE [Lu et al, 2019] distinguishes the meta-path based relations and deals with them using different models. HeteSpaceyWalk [He et al, 2019] proposes a spacey random walk to preserve the Markov chain nature of meta-paths based random walks.…”

Section: Related Workmentioning

confidence: 99%

Network Schema Preserving Heterogeneous Information Network Embedding

Zhao

Wang

Shi

et al. 2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

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102

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As heterogeneous networks have become increasingly ubiquitous, Heterogeneous Information Network (HIN) embedding, aiming to project nodes into a low-dimensional space while preserving the heterogeneous structure, has drawn increasing attention in recent years. Many of the existing HIN embedding methods adopt meta-path guided random walk to retain both the semantics and structural correlations between different types of nodes. However, the selection of meta-paths is still an open problem, which either depends on domain knowledge or is learned from label information. As a uniform blueprint of HIN, the network schema comprehensively embraces the high-order structure and contains rich semantics. In this paper, we make the first attempt to study network schema preserving HIN embedding, and propose a novel model named NSHE. In NSHE, a network schema sampling method is first proposed to generate sub-graphs (i.e., schema instances), and then multi-task learning task is built to preserve the heterogeneous structure of each schema instance. Besides preserving pairwise structure information, NSHE is able to retain high-order structure (i.e., network schema). Extensive experiments on three real-world datasets demonstrate that our proposed model NSHE significantly outperforms the state-of-the-art methods.

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“…More recently, there is another line of HIN embedding study, decomposing based methods. These methods learn node embeddings by decomposing HIN semantics into projected metrics with different relation spaces (such as PME [8], HEER [30] and RHINE [20]), or decomposing HINs into cross-network relationships in different sub-networks (such as AMN [26], DME [23] and AspEm [29]). However, these methods are not suitable for HINs that have a large number of types, since the metrics projecting or sub-networks decomposing are conducted in type-pair-specific level, leading in the exponential growth of spaces with respect to the number of types.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Embedding Framework for Heterogeneous Information Networks

Chen

Ding

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Heterogeneous information networks (HINs) have been ubiquitous in the real-world. HIN embeddings, which encode various information of the networks into low-dimensional vectors, can facilitate a wide range of applications on graph-structured data. Existing HIN embedding methods include random walk based methods that may not fully utilize the edge semantics and knowledge graph embedding methods that restrict the expression ability of topological information. In this paper, we propose a novel adaptive embedding framework, which integrates these two kinds of methods to preserve both topological information and relational information. By incorporating an assistant knowledge graph embedding model, the proposed framework performs efficient biased random walk under the guidance of edge semantics. Meanwhile, the short and long dependency information can be adaptively preserved for diverse networks. Furthermore, a patient joint training strategy is proposed to make the framework flexible and adapt to different assistant knowledge graph embedding models. Extensive experiments on five real-world datasets demonstrate that the proposed method can adaptively sample node contexts and outperforms several stateof-the-art methods for node classification and link prediction tasks. CCS CONCEPTS • Computing methodologies → Learning latent representations; • Information systems → Data mining.

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Relation Structure-Aware Heterogeneous Information Network Embedding

Cited by 111 publications

References 5 publications

Relational Topology-Based Heterogeneous Network Embedding for Predicting Drug-Target Interactions

Relational Topology-Based Heterogeneous Network Embedding for Predicting Drug-Target Interactions

Network Schema Preserving Heterogeneous Information Network Embedding

An Adaptive Embedding Framework for Heterogeneous Information Networks

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