Link prediction in heterogeneous information networks: An improved deep graph convolution approach

Wang, Xi; Chai, Yibo; Li, Hui; Wu, Danqin

doi:10.1016/j.dss.2020.113448

Cited by 25 publications

(9 citation statements)

References 46 publications

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“…According to [22], the solution is proposed to rank and predict links in a network such that it expands the random walks via a distinct restart probability for each node. The results on two datasets reveal that the proposed method outperforms the classic random walk with restart (RWR) regarding link prediction.…”

Section: Related Workmentioning

confidence: 99%

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A Hybrid Clustering Approach for link prediction in Heterogeneous Information Networks

Sajjadi

Esmaeili

Ghobaei‐Arani

et al. 2023

Preprint

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In recent years, researchers from academic and industrial fields have become increasingly interested in social network data to extract meaningful information. This information is used in applications such as link prediction between people groups, community detection, protein module identification, etc. Therefore, the clustering technique has emerged as a solution to finding similarities between social network members. Recently, in most graph clustering solutions, the structural similarity of nodes is combined with their attribute similarity. The results of these solutions indicate that the graph's topological structure is more important. Since most social networks are sparse, these solutions often suffer from insufficient use of node features. This paper proposes a hybrid clustering approach for link prediction in heterogeneous information networks (HINs). In our approach, an adjacency vector is determined for each node until, in this vector, the weight of the direct edge or the weight of the shortest communication path among every pair of nodes is considered. A similarity metric is presented that calculates similarity using the direct edge weight between two nodes and the correlation between their adjacency vectors. Finally, we evaluated the effectiveness of our proposed method using DBLP and Political blogs datasets under entropy, density, purity, and execution time metrics. The simulation results demonstrate that while maintaining the cluster density significantly reduces the entropy and the execution time compared with the other methods.

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Section: Related Workmentioning

confidence: 99%

“…According to the output of line 4, structural similarity and attribute (lines 6-17) are repeated for both vertices. Then, the hybrid similarity and distance function of each pair of vertices will be calculated (lines [17][18][19][20][21][22] and finally, will be done clustering process (line 23).…”

Section: Proposed Graph Clustering Algorithmmentioning

confidence: 99%

A Hybrid Clustering Approach for link prediction in Heterogeneous Information Networks

Sajjadi

Esmaeili

Ghobaei‐Arani

et al. 2023

Preprint

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“…The nodes in a heterogeneous graph include users and items, and the edges consist of the interaction behaviors between users and items. Because heterogeneous graph neural networks are very flexible in modeling heterogeneous data [33,34], they are often used to represent rich auxiliary information in recommendations. With the representation of heterogeneous graph neural networks, a recommendation system can be viewed as a similarity search of meta-paths on heterogeneous graph neural networks [35].…”

Section: Heterogeneous Graphical Neural Networkmentioning

confidence: 99%

Meta-path Discovering based Recommendation Model via Heterogeneous Graph Neural Networks

Sastry

Goh

Leung

et al. 2022

Preprint

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In the era of information explosion, information overload has become a challenge for people to find information that interests them. Recommendation systems, as a kind of information filtering system, can understand users’ interests based on their personal data or historical behavior records, and are widely used in Web applications such as e-commerce, search, and streaming websites. However, traditional GNN-based recommendation algorithms can only handle regular topological graphs composed of a single type of nodes, while the data in today’s network is not composed of only a single type of nodes. In addition, traditional GNNs only fuse the first-order neighborhood features of nodes and cannot obtain the deeper structural relationships of nodes in the network. Therefore, when the data set is sparse and each node has only a very small number of neighbors, the recommendation quality of the traditional GNNbased recommendation algorithm decreases significantly. Heterogeneous graph neural networks provide new directions and ideas for recommendation research, and bring new challenges to data mining methods of heterogeneous graphs. Although the recommendation algorithm based on heterogeneous graph neural network is mainly based on the similarity of meta-paths, this method cannot effectively extract and utilize the rich structural and semantic information in heterogeneous graphs for recommendation, although the performance of the recommendation model can be improved by using the similarity of meta-paths. We proposed a joint multi-feature representation of information in heterogeneous information networks. For both user/item interaction and user socialization domains, the representation of node features in the case of sparse network connectivity is enhanced by using multi-order topological structure information in heterogeneous information networks. A graph neural network recommendation method oriented to attention mechanism is proposed. The recommendation quality of the recommendation model is enhanced in the case of sparse scoring matrix through the effective fusion of multidimensional representation vectors of users, items, ratings, and social. By conducting experiments on multiple public data sets, it is verified that the recommendation performance of our model in this paper significantly better than the baseline methods.

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“…Because only the direct neighbors of the miRNA were considered as the criterion for miRNA functional similarity score, the prediction effect was limited. To increase the accuracy of miRNA–disease association prediction, Xuan et al [ 7 ] proposed the weighted k-nearest neighbor method (HDMP). Chen et al developed the computational framework of RWRMDA that performs random walk on the miRNA network to predict novel disease-related miRNAs.…”

Section: Introductionmentioning

confidence: 99%

Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA–disease association prediction

Huang

Zhang

et al. 2022

BMC Bioinformatics

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Background A large number of evidences from biological experiments have confirmed that miRNAs play an important role in the progression and development of various human complex diseases. However, the traditional experiment methods are expensive and time-consuming. Therefore, it is a challenging task that how to develop more accurate and efficient methods for predicting potential associations between miRNA and disease. Results In the study, we developed a computational model that combined heterogeneous graph convolutional network with enhanced layer for miRNA–disease association prediction (HGCNELMDA). The major improvement of our method lies in through restarting the random walk optimized the original features of nodes and adding a reinforcement layer to the hidden layer of graph convolutional network retained similar information between nodes in the feature space. In addition, the proposed approach recalculated the influence of neighborhood nodes on target nodes by introducing the attention mechanism. The reliable performance of the HGCNELMDA was certified by the AUC of 93.47% in global leave-one-out cross-validation (LOOCV), and the average AUCs of 93.01% in fivefold cross-validation. Meanwhile, we compared the HGCNELMDA with the state‑of‑the‑art methods. Comparative results indicated that o the HGCNELMDA is very promising and may provide a cost‑effective alternative for miRNA–disease association prediction. Moreover, we applied HGCNELMDA to 3 different case studies to predict potential miRNAs related to lung cancer, prostate cancer, and pancreatic cancer. Results showed that 48, 50, and 50 of the top 50 predicted miRNAs were supported by experimental association evidence. Therefore, the HGCNELMDA is a reliable method for predicting disease-related miRNAs. Conclusions The results of the HGCNELMDA method in the LOOCV (leave-one-out cross validation, LOOCV) and 5-cross validations were 93.47% and 93.01%, respectively. Compared with other typical methods, the performance of HGCNELMDA is higher. Three cases of lung cancer, prostate cancer, and pancreatic cancer were studied. Among the predicted top 50 candidate miRNAs, 48, 50, and 50 were verified in the biological database HDMMV2.0. Therefore; this further confirms the feasibility and effectiveness of our method. Therefore, this further confirms the feasibility and effectiveness of our method. To facilitate extensive studies for future disease-related miRNAs research, we developed a freely available web server called HGCNELMDA is available at http://124.221.62.44:8080/HGCNELMDA.jsp.

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Link prediction in heterogeneous information networks: An improved deep graph convolution approach

Cited by 25 publications

References 46 publications

A Hybrid Clustering Approach for link prediction in Heterogeneous Information Networks

A Hybrid Clustering Approach for link prediction in Heterogeneous Information Networks

Meta-path Discovering based Recommendation Model via Heterogeneous Graph Neural Networks

Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA–disease association prediction

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