Heterogeneous Graph Embedding Research Articles

Hierarchical bottleneck for heterogeneous graph representation

Heterogeneous graphs (HGs) contain many nodes and their interaction relationships, which can model complex systems and provide rich semantic and structural information for task execution. Among these, HG representation stands as the fundamental and pivotal component. Existing HG representation methods primarily employ graph neural networks to acquire the semantics of nodes along various meta-paths and fuse them to represent the nodes. The most prevalent HG representation methods encompass two steps: semantic information extraction within meta-paths and semantic fusion between meta-paths. However, these methods overlooked the consideration of node heterogeneity within meta-paths and the simultaneous semantic correlation between meta-paths. Specifically, node heterogeneity within meta-paths signifies that the meta-path-based neighbors do not consistently contain information that positively influences the target node, and the semantic correlation between meta-paths indicates that different meta-path spaces are not entirely independent. Disregarding either of these issues leads to the propagation of irrelevant or redundant information and potential disruption of HG embedding. Consequently, in this study, we propose the HBHG, which is a hierarchical bottleneck for heterogeneous graph representation. HBHG primarily employs the information bottleneck (IB) as a guiding principle, constraining the propagation of irrelevant information within and between meta-paths while preserving relevant information. The central concept of the IB revolves around viewing model learning as the preservation of relevant information and compression of irrelevant information, accomplished by minimizing the dependency between input and hidden features through mutual information (MI) and maximizing the dependency between hidden features and ground-truth. Considering the complexity associated with MI estimation, this paper introduces a novel dependency index, namely the Hilbert-Schmidt independence criterion (HSIC), which offers ease of calculation. Specifically, HBHG comprises two primary components: a semantic bottleneck within meta-paths and a semantic bottleneck between meta-paths. The semantic bottleneck within meta-paths relies primarily on the HSIC-based limitations of dependencies at different layers of the graph neural network on various meta-paths, thereby maximizing the extraction of information relevant to the target node from neighboring nodes. The semantic bottleneck between meta-paths enables flexible extraction and fusion of semantic information based on downstream tasks, achieved by managing the trade-off of dependencies with HSIC between different meta-path semantic spaces. In summary, the proposed HBHG integrates hierarchical bottleneck constraints within and between meta-paths. This integration serves to maximize the aggregation of relevant information while effectively compressing irrelevant information, thereby enhancing the quality of heterogeneous graph embedding. The effectiveness of HBHG was validated through performance and ablation experiments conducted on multiple datasets.

A malware detection model based on imbalanced heterogeneous graph embeddings

The proliferation of malware in recent years has posed a significant threat to the security of computers and mobile devices. Detecting malware, especially on the Android platform, has become a growing concern for researchers and the software industry. This paper proposes a new method for detecting Android malware based on unbalanced heterogeneous graph embedding. First of all, most malware datasets contain an imbalance of malicious and benign samples, since some types of malware are scarce and difficult to collect. Thus, as a result of this problem, the classification algorithm is unable to analyze the minority samples through sufficient data, resulting in poor downstream classifier performance, in light of the fact that adversarial generation networks possess the characteristic of completing data, an algorithm for generating graph structure data is presented, in which nodes are generated to simulate the distribution of minority nodes within a network topology. Then, considering that heterogeneous information networks have the characteristics of retaining rich node semantic features and mining implicit relationships, heterogeneous graphs are used to construct models for different types of entities (i.e. Apps, APIs, permissions, intents, etc.) and different meta-paths. Finally, a new method is introduced to alleviate the over-smoothing phenomenon of node information in the propagation of deep network. In the deep GCN, we first sample the leader nodes of each layer node, and then add a residual connection and an identity map in order to determine the characteristics of the high-order leader. In this paper, a self-attention-based semantic fusion method is also applied to adaptively fuse embedded representations of software nodes under different meta-paths. The test results demonstrate that the proposed IHODroid model effectively detects malicious software. In the DREBIN dataset, which consists of 123,453 Android applications and 5,560 malicious samples, the IHODroid model achieves an accuracy of 0.9360 and an F1 score of 0.9360, outperforming other state-of-the-art baseline methods.

Review of heterogeneous graph embedding methods based on deep learning techniques and comparing their efficiency in node classification

Review of heterogeneous graph embedding methods based on deep learning techniques and comparing their efficiency in node classification

Prediction of Drug-Disease Associations Based on Multi-Kernel Deep Learning Method in Heterogeneous Graph Embedding.

Computational drug repositioning can identify potential associations between drugs and diseases. This technology has been shown to be effective in accelerating drug development and reducing experimental costs. Although there has been plenty of research for this task, existing methods are deficient in utilizing complex relationships among biological entities, which may not be conducive to subsequent simulation of drug treatment processes. In this article, we propose a heterogeneous graph embedding method called HMLKGAT to infer novel potential drugs for diseases. More specifically, we first construct a heterogeneous information network by combining drug-disease, drug-protein and disease-protein biological networks. Then, a multi-layer graph attention model is utilized to capture the complex associations in the network to derive representations for drugs and diseases. Finally, to maintain the relationship of nodes in different feature spaces, we propose a multi-kernel learning method to transform and combine the representations. Experimental results demonstrate that HMLKGAT outperforms six state-of-the-art methods in drug-related disease prediction, and case studies of five classical drugs further demonstrate the effectiveness of HMLKGAT.

Reorganizing heterogeneous information from host–microbe interaction reveals innate associations among samples

AbstractThe information on host–microbe interactions contained in the operational taxonomic unit (OTU) abundance table can serve as a clue to understanding the biological traits of OTUs and samples. Some studies have inferred the taxonomies or functions of OTUs by constructing co‐occurrence networks, but co‐occurrence networks can only encompass a small fraction of all OTUs due to the high sparsity of the OTU table. There is a lack of studies that intensively explore and use the information on sample‐OTU interactions. This study constructed a sample‐OTU heterogeneous information network and represented the nodes in the network through the heterogeneous graph embedding method to form the OTU space and sample space. Taking advantage of the represented OTU and sample vectors combined with the original OTU abundance information, an Integrated Model of Embedded Taxonomies and Abundance (IMETA) was proposed for predicting sample attributes, such as phenotypes and individual diet habits. Both the OTU space and sample space contain reasonable biological or medical semantic information, and the IMETA using embedded OTU and sample vectors can have stable and good performance in the sample classification tasks. This suggests that the embedding representation based on the sample‐OTU heterogeneous information network can provide more useful information for understanding microbiome samples. This study conducted quantified representations of the biological characteristics within the OTUs and samples, which is a good attempt to increase the utilization rate of information in the OTU abundance table, and it promotes a deeper understanding of the underlying knowledge of human microbiome.

Heterogeneous Graph Neural Network With Multi-View Representation Learning

In recent years, graph neural networks (GNNs)-based methods have been widely adopted for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich information from a HG into the low-dimensional node embeddings. However, previous works usually easily fail to fully leverage the inherent heterogeneity and rich semantics contained in the complex local structures of HGs. On the one hand, most of the existing methods either inadequately model the local structure under specific semantics, or neglect the heterogeneity when aggregating information from the local structure. On the other hand, representations from multiple semantics are not comprehensively integrated to obtain node embeddings with versatility. To address the problem, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Heterogeneous Graph Neural Network</i> for HG embedding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">within a Multi-View representation learning framework</i> (named MV-HetGNN), which consists of a view-specific ego graph encoder and auto multi-view fusion layer. MV-HetGNN thoroughly learns complex heterogeneity and semantics in the local structure to generate comprehensive and versatile node representations for HGs. Extensive experiments on three real-world HG datasets demonstrate the significant superiority of our proposed MV-HetGNN compared to the state-of-the-art baselines in various downstream tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</i> . node classification, node clustering, and link prediction.

RHGNN: Fake reviewer detection based on reinforced heterogeneous graph neural networks

In e-commerce, fake reviewers frequently post fake reviews to mislead consumers into making unwise shopping decisions, seriously affecting customers’ benefits. Graph neural networks (GNNs) have been widely cultivated in fake reviewer detection in recent years, but they exhibit two main limitations. Firstly, most approaches model reviewer detection with a homogeneous graph, ignoring the interdependent clues among reviewers, reviews, and products, making them ineffective against stealthy fake reviewers. Secondly, few works noticed the camouflage behavior of fake reviewers, i.e., fake reviewers disguise themselves as authentic ones by establishing links with genuine reviewers, which extremely weakens the suspiciousness of fake reviewers and undermines detection performance. This paper proposes RHGNN, a robust fake reviewers detection framework based on Reinforced Heterogeneous Graph Neural Networks. Particularly, we first model fake reviewer detection with a heterogeneous graph consisting of reviews, reviewers, and products, to capture stealthy fraud clues by investigating their heterogeneous interactive relations. Subsequently, we present a reinforced neighbor selector (RNS) to sample and aggregate top−k informative neighbors while filtering out the camouflaged relationships. Finally, we propose a self-supervised heterogeneous graph embedding method based on mutual information to learn the discriminative features of fake reviewers. Experimental results show that RHGNN demonstrates promising and robust performance, especially in detecting well-disguised fake reviewers.

HGECDA: A Heterogeneous Graph Embedding Model for CircRNA-Disease Association Prediction.

Circular RNAs (circRNAs) are specifically and abnormally expressed in disease tissues, and thus can be used as biomarkers to diagnose relevant diseases. Predicting circRNA-disease associations will provide essential clues to reveal molecular mechanisms of disease development and discover novel therapeutic targets. Existing algorithms ignore the heterogeneous biological association information related to microRNAs (miRNAs). Based on a heterogeneous graph embedding model, a novel circRNA-disease association prediction method called HGECDA is developed in this paper. The heterogeneous graph network containing circRNA-miRNA-disease association information is first constructed. To sample the heterogeneous information, the meta-path-based random walk that can capture the relevance between various types of nodes is employed. Then, the path embedding model based on skip-gram and random negative sampling is built to acquire the initial feature vectors of circRNAs and diseases. Finally, the CosMulformer model with linearized self-attention and Hadamard product is designed to obtain the circRNA-disease interaction vectors and conduct the prediction task. Experimental results demonstrate the critical role of miRNA in enriching the information of the feature space, the effectiveness of the CosMulformer model in picking out deep local interaction features, and the feasibility of the Hadamard product chosen as the integration pattern in the CosMulformer model. Compared with existing state-of-the-art methods on the same dataset, HGECDA performs better than the other seven algorithms. Moreover, the case studies about breast cancer and colorectal cancer demonstrate the practical value of HGECDA in predicting potential circRNA-disease associations.

DapBCH: a disease association prediction model Based on Cross-species and Heterogeneous graph embedding.

The study of comorbidity can provide new insights into the pathogenesis of the disease and has important economic significance in the clinical evaluation of treatment difficulty, medical expenses, length of stay, and prognosis of the disease. In this paper, we propose a disease association prediction model DapBCH, which constructs a cross-species biological network and applies heterogeneous graph embedding to predict disease association. First, we combine the human disease-gene network, mouse gene-phenotype network, human-mouse homologous gene network, and human protein-protein interaction network to reconstruct a heterogeneous biological network. Second, we apply heterogeneous graph embedding based on meta-path aggregation to generate the feature vector of disease nodes. Finally, we employ link prediction to obtain the similarity of disease pairs. The experimental results indicate that our model is highly competitive in predicting the disease association and is promising for finding potential disease associations.

Protein Complex Identification Based on Heterogeneous Protein Information Network.

Protein complexes are the foundation of all cellular activities, and accurately identifying them is crucial for studying cellular systems. The efficient discovery of protein complexes is a focus of research in the field of bioinformatics. Most existing methods for protein complex identification are based on the structure of the protein-protein interaction (PPI) network, whereas some methods attempt to integrate biological information to enhance the features of the protein network for complex identification. Existing protein complex identification methods are unable to fully integrate network topology information and biological attribute information. Most of these methods are based on homogeneous networks and cannot distinguish the importance of different attributes and protein nodes. To address these issues, a GO attribute Heterogeneous Attention network Embedding (GHAE) method based on heterogeneous protein information networks is proposed. First, GHAE incorporates Gene Ontology (GO) information into the PPI network, constructing a heterogeneous protein information network. Then, GHAE uses a dual attention mechanism and heterogeneous graph convolutional representation learning method to learn protein features and to identify protein complexes. The experimental results show that building heterogeneous protein information networks can fully integrate valuable biological information. The heterogeneous graph embedding learning method can simultaneously mine the features of protein and GO attributes, thereby improving the performance of protein complex identification.

OSGNN: Original graph and Subgraph aggregated Graph Neural Network

Heterogeneous Graph Embedding (HGE) is receiving a great attention from researchers, as it can be widely and effectively used to solve problems from various real-world applications. The existing HGE models mainly learn node representation directly on the whole heterogeneous graph by aggregating neighboring information, which unavoidably leads to the loss of useful high-order information. Another mainstream is to split heterogeneous graphs into different homogeneous subgraphs and then learn representations separately. However, this isolated handling way is prone to the loss of important interactions between the nodes of the same type. To address the above challenging but interesting problems, we propose an Original graph and Subgraph aggregated Graph Neural Network (OSGNN). Specifically, we first split the original heterogeneous graph into several subgraphs, and then weighted combine them to get a new meaningful homogeneous graph. Finally, the first-order and high-order information of the target node are learned from the original heterogeneous graph and the homogeneous subgraph respectively and concatenated as the final node representation. Extensive experiments on three real-world heterogeneous graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods. The source codes of this work are available on https://github.com/ZZY-GraphMiningLab/OSGNN.

Adaptive Neighbor Graph Aggregated Graph Attention Network for Heterogeneous Graph Embedding

Graph attention network can generate effective feature embedding by specifying different weights to different nodes. The key of the research on heterogeneous graph embedding is the way to combine its rich structural information with semantic relations to aggregate the neighborhood information. Most of the existing heterogeneous graph representation learning methods guide the selection of neighbors by defining various meta-paths on heterogeneous graphs. However, these models only consider the information contained in the nodes under different paths and ignore the potential semantic relationships of nodes in different neighbor graph structures, which leads to the underutilization of graph structure information. In this paper, we propose a novel adaptive framework named Neighbor Graph Aggregated Graph Attention Network (NGGAN) to fully exploit graph topological details in heterogeneous graph, and aggregates their information to obtain an effective embedding. The key idea is to use different levels of sampling methods to define neighborhood, and use neighbor graphs to represent the complex structural interaction between nodes. In this way, the high-order relationship between nodes and the latent semantics of neighbor graphs can be fully explored. Afterwards, a hierarchical attention mechanism is applied to adaptively learn the importance of different objects, including node information, path information and neighbor graph information. Multiple downstream tasks are performed on four real-world heterogeneous graph datasets, and the experimental results demonstrate the effectiveness of NGGAN.

MIGTNet: Metapath Instance-based Graph Transformation Network for heterogeneous graph embedding

Many real-world graphs are heterogeneous with various types of entities and relations. These heterogeneous graphs commonly inherent various structural and semantic information of sequences of node types, called metapaths; and many current heterogeneous graph embedding models learn not only the node features but also the metapaths. However, all of them only focus on the metapath instances (i.e., the sequences of nodes following the schema defined by a metapath) and ignore the relations between them when learning about the metapaths. To overcome this limitation, we propose a new model called the Metapath Instance-based Graph Transformation Network (MIGTNet) for heterogeneous graph embedding, which uses both metapath instances and relations between them. MIGTNet constructs a metapath instance-based graph, where a node represents a metapath instance and a link represents a relation between metapath instances, and inputs it to a hierarchical graph attention network to obtain meaningful node embeddings. Extensive experiments using real datasets show that MIGTNet outperforms state-of-the-art heterogeneous graph embedding models in node classification and node clustering.

A survey on graph embedding techniques for biomedical data: Methods and applications

As a result of the expeditious advancement of biomedical technologies, a plethora of relational data linking biomedical entities such as genes, proteins, and drugs have been collected for modern biomedical research. Biomedical graphs, one of the most popular ways to represent relational data, can easily describe different complex biomedical systems, including molecular-level, multi-omics-level, therapeutics-level, and healthcare-level interactions. However, traditional graph analysis methods still suffer from two difficulties (i.e., heterogeneities and dynamic properties of biomedical graphs) when handling high-dimensional, multi-modal, and sparsely interconnected biomedical data. To address these issues, graph embedding methods that can effectively analyze biomedical graphs have received a significant amount of attention recently. Generally, graph-based data is converted into a low-dimensional vector space with its structural properties and well-reserved information. These vectorized representations are used for further computation in various downstream biomedical tasks, such as gene function prediction and drug–target interaction prediction. In this article, we focus on the application of graph data in the biomedical domain and mainly introduce recent developments of graph embedding techniques (homogeneous, heterogeneous, and dynamic graph embedding techniques), including methodologies and related biomedical tasks. We also summarize relevant biomedical datasets and open-source implementations. We further discuss existing limitations and potential solutions. We hope this survey can provide useful directions for researchers who are interested in using graph embedding methods to solve problems in the biomedical field.

Graph-DTI: A New Model for Drug-Target Interaction Prediction Based on Heterogenous Network Graph Embedding.

In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topology-maintaining representations of drugs and targets, thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However, the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction, there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. We studied the drug-drug interaction (DDI) and DTI from DrugBank Version 3.0, protein-protein interaction (PPI) from the human protein reference database Release 9, drug structure similarity from Morgan fingerprints of radius 2 and calculated by RDKit, and protein sequence similarity from Smith-Waterman score. Our study consists of three major components. First, various drugs and target proteins were integrated, and a heterogeneous network was established based on a series of data sets. Second, the graph neural networks-inspired graph auto-encoding method was used to extract high-order structural information from the heterogeneous networks, thereby revealing the description of nodes (drugs and proteins) and their topological neighbors. Finally, potential DTI prediction was made, and the obtained samples were sent to the classifier for secondary classification. The performance of Graph-DTI and all baseline methods was evaluated using the sums of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC). The results indicated that Graph-DTI outperformed the baseline methods in both performance results. Compared with other baseline DTI prediction methods, the results showed that Graph-DTI had better prediction performance. Additionally, in this study, we effectively classified drugs corresponding to different targets and vice versa. The above findings showed that Graph-DTI provided a powerful tool for drug research, development, and repositioning. Graph-DTI can serve as a drug development and repositioning tool more effectively than previous studies that did not use heterogeneous network graph embedding.

Cold-start item recommendation for representation learning based on heterogeneous information networks with fusion side information

In recommendation systems, the cold-start problem refers to the situation that users or items have too few interaction events with the system to be recommended, such as clicks or ratings. The cold-start problem leads to the loss of new users and difficulty in recommending new items and weakens the performance of recommendations. In this paper, we focus on alleviating the cold-start problem of items. Previous work has used the structure of item homogeneous graphs to establish connections between items and generate item embeddings. However, the homogeneous graph of a single node cannot fully explore the potential structural features between users and goods. Due to the flexibility of heterogeneous data, recommendation systems can also employ the Heterogeneous Information Network(HIN) to characterize complex, heterogeneous data. In this paper, a novel Heterogeneous Graph Embedding Learning based Skip-Gram (HSG) model is proposed to mitigate the cold-start problem. First, we introduce three different types of subnetworks and combine them to construct heterogeneous graphs. Then meaningful sequences of nodes are generated via meta-path-based random walk for network embedding. Finally, a Skip-Gram model incorporating side information is used to learn item representations. The learned node embeddings are used as pre-training for the Matrix Factorization (MF) model. The HSG model is proven to be effective by extensive experiments on four publicly available datasets.

Semantic-guided graph neural network for heterogeneous graph embedding

Heterogeneous Graph Neural Network (HGNN) has shown a great promising in embedding complex structural and semantic information of heterogeneous graph. However, many popular HGNNs fail to capture the meaningful characteristics to distinguish heterogeneous nodes, known as the semantic confusion problem. In this paper, we hold that semantic confusion problem can be addressed by jumping knowledge toward semantics enrichment, and propose a general framework for heterogeneous graph embedding named Semantic-guided Graph Neural Network (SGNN). Here, we develop novel two-level fusion mechanisms in both node and semantic aggregation. In the node-level, we aggregate the local neighbors with jumping knowledge to learn an enhanced local representation. In the semantic-level, we maximize the common representation to extract the jumping knowledge from multiple semantics in latent space. The common representation is injected into semantic-level aggregation as jumping knowledge to guide the model to pay more attention on target semantics. In the end, the semantic confusion problem is shown to be alleviated in the two-level semantic-guided aggregation framework of SGNN through both theory and experiments. Experimental results demonstrate that SGNN gains highest results in real-world tasks. We also perform validation experiments to evaluate the effectiveness of aggregation mechanism towards semantic confusion in each level, and the results show superiority of SGNN.

Semantic Tradeoff for Heterogeneous Graph Embedding

Recently, many data mining models based on heterogeneous graph (HG) have emerged. Among these, HG embedding is an important and indispensable process. However, the existing HG embedding models usually use graph neural network to learn embeddings separately on different meta-paths, ignoring the fact that different meta-paths contain not only their unique semantics but also related semantics. This may result in semantic overlap or irrelevance, which needs to be a feasible and effective tradeoff for high-quality HG embedding, yet studies thereof have rarely been reported. In this article, we propose semantic tradeoff HG embedding (STHGE) by first introducing the Hilbert-Schmidt independence criterion (HSIC) as restriction. The main idea of STHGE is to regard semantic tradeoff as independence tradeoff (or correlation) between different meta-path spaces. Specifically, we first transform the original features of nodes into different meta-path feature spaces with HSIC restriction between them. Then, we use graph attention network to learn the embeddings of nodes on different meta-paths with HSIC restrictions. Finally, we concatenate the embeddings on different meta-paths to perform prediction. Experimental results on three heterogeneous datasets not only demonstrate the effectiveness of STHGE but also demonstrate that STHGE can achieve a new semantic tradeoff between different meta-paths. Furthermore, we demonstrate the robustness of STHGE.

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

Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks (e.g., node/graph classification, node clustering, link prediction), has drawn considerable attentions in recent years. In this survey, we perform a comprehensive review of the recent development on HG embedding methods and techniques. We first introduce the basic concepts of HG and discuss the unique challenges brought by the heterogeneity for HG embedding in comparison with homogeneous graph representation learning; and then we systemically survey and categorize the state-of-the-art HG embedding methods based on the information they used in the learning process to address the challenges posed by the HG heterogeneity. In particular, for each representative HG embedding method, we provide detailed introduction and further analyze its pros and cons; meanwhile, we also explore the transformativeness and applicability of different types of HG embedding methods in the real-world industrial environments for the first time. In addition, we further present several widely deployed systems that have demonstrated the success of HG embedding techniques in resolving real-world application problems with broader impacts. To facilitate future research and applications in this area, we also summarize the open-source code, existing graph learning platforms and benchmark datasets. Finally, we explore the additional issues and challenges of HG embedding and forecast the future research directions in this field.

Relation-Aware Weighted Embedding for Heterogeneous Graphs

Heterogeneous graph embedding, aiming to learn the low-dimensional representations of nodes, is effective in many tasks, such as link prediction, node classification, and community detection. Most existing graph embedding methods conducted on heterogeneous graphs treat the heterogeneous neighbours equally. Although it is possible to get node weights through attention mechanisms mainly developed using expensive recursive message-passing, they are difficult to deal with large-scale networks. In this paper, we propose R-WHGE, a relation-aware weighted embedding model for heterogeneous graphs, to resolve this issue. R-WHGE comprehensively considers structural information, semantic information, meta-paths of nodes and meta-path-based node weights to learn effective node embeddings. More specifically, we first extract the feature importance of each node and then take the nodes’ importance as node weights. A weighted random walks-based embedding learning model is proposed to generate the initial weighted node embeddings according to each meta-path. Finally, we feed these embeddings to a relation-aware heterogeneous graph neural network to generate compact embeddings of nodes, which captures relation-aware characteristics. Extensive experiments on real-world datasets demonstrate that our model is competitive against various state-of-the-art methods.