Nodes In Heterogeneous Information Network Research Articles

A Graph Contrastive Learning Model Based on Structural and Semantic View for HIN Recommendation

With the rapid growth of information in the Internet era, people are in great need of recommendation methods to filter information. At present, recommendation methods which based on heterogeneous information network (HIN) have attracted wide attention. Recently, HIN-based recommendation methods need to be modeled from two aspects: node structural association and semantic association. To this end, we propose a graph contrastive learning model based on structural and semantic view for HIN recommendation (GCL-SS). GCL-SS utilizes U-I interactive view to obtain node structural embeddings, and utilizes U-I semantic view to obtain node semantic embeddings. Based on these two kinds of embeddings, we establish a self-supervised contrastive learning mechanism to effectively integrate structural information and semantic information of user (item) nodes in HIN, and finally learn a more discriminative user (item) embedding. In addition, in order to strengthen the semantic association between nodes, we innovatively utilize time sequence encoder (TSE), such as LSTM, to encode semantic homogeneous network decomposed by HIN in U-I semantic view. At last, based on the user and item embeddings, we adopt bilinear decoder to model the potential association between user and item, so as to realize rating prediction of user to item. The experimental results on three real datasets confirm that our GCL-SS model performs better than state-of-the-art recommendation methods in rating prediction task. In addition, the results of four ablation experiments indicate that our GCL-SS model can effectively improve the performance of rating prediction in recommendation.

OpenMetaRec: Open-metapath heterogeneous dual attention network for paper recommendation

Paper recommendation based on author preferences has gained widespread attention. Current methods focus on the heterogeneous information network (HIN) to infer correlation clues based on the metapath with single-type association (closed-metapath). However, the closed-metapath approach only considers sparse correlations between a few type nodes, which brings great challenges for preference learning. To address this issue, we propose a novel metapath with multiple-type correlations (open-metapath) approach for paper recommendation to capture rich correlations between various type nodes in HIN. First, the academic heterogeneous information network is transformed into open-metapaths to acquire whole semantic correlations among varying type nodes, enriching sparse associations. Then, neighbor attention is designed to explore highly relevant structural and semantic information, which initializes author preferences and paper features. Next, metapath attention is constructed to measure the impact intensity of open-metapaths and distinguish their representations. Finally, static and interactive features are fused to learn vectors of authors and papers, providing desired papers. Substantial experiments demonstrate that the proposed model outperforms other existing models significantly.

A Reinforcement Learning List Recommendation Model Fused with Graph Neural Networks

Existing list recommendation methods present a list consisting of multiple items for feedback recommendation to user requests, which has the advantages of high flexibility and direct user feedback. However, the structured representation of state data limits the embedding of users and items, making them isolated from each other, missing some useful infomation for recommendation. In addition, the traditional non-end-to-end learning series takes a long time and accumulates errors. During the model training process, the results of each task can easily affect the next calculation, thus affecting the entire training effect. Aiming at the above problems, this paper proposes a Reinforcement Learning List Recommendation Model Fused with a Graph Neural Network, GNLR. The goal of this model is to maximize the recommendation effect while ensuring that the list recommendation system accurately analyzes user preferences to improve user experience. To this end, firstly, we use an user–item bipartite graph and Graph Neural Network to aggregate neighborhood information for users and items to generate graph structured representation; secondly, we adopt an attention mechanism to assign corresponding weights to neighborhood information to reduce the influence of noise nodes in heterogeneous information networks; finally, we alleviate the problems of traditional non-end-to-end methods through end-to-end training methods. The experimental results show that the method proposed in this paper can alleviate the above problems, and the recommendation hit rate and accuracy rate increase by about 10%.

Prerequisite-driven Fair Clustering on Heterogeneous Information Networks

This paper studies the problem of fair clustering on heterogeneous information networks (HINs) by considering constraints on structural and sensitive attributes. We propose a Prerequisite-driven Fair Clustering (PDFC ) algorithm to solve this problem. Specifically, we define the structural constraint on the connection among nodes in HINs by combining meta-paths and prerequisite meta-paths and introduce Fairlets as the balance constraint. Under two constraints, we learn node embeddings based on graph models and perform theCholesky decomposition to obtain their orthogonal embeddings. We fuse node embeddings under constraints, define the loss function of PDFC, and perform k-means to achieve clustering. In addition, we design an update strategy of the adjacency matrix to achieve dynamic PDFC over time. Compared with several fair clustering algorithms on three real-world datasets, our experimental results verify the effectiveness and efficiency of PDFC.

An effective heterogeneous information network representation learning framework

Given the heterogeneity of real-world networks and the low efficiency of directly mining networks, heterogeneous information network (HIN) representation learning, which learns low-dimensional embeddings of nodes to represent various structural and semantic information in HINs, becomes a very crucial topic. Existing HIN based representation learning methods either omit the global information of networks, or only consider the first-order neighbors of each node. The two problems reflect the insufficient extraction and exploitation of network information for HIN embedding. To address these problems, we propose a novel Heterogeneous Graph SAmple and aggreGatE framework, named HGSAGE, for learning low-dimensional similarity-preserved embeddings of nodes in HINs. This framework consists of three mechanisms, i.e., the gravity model based on the meta-path reachable graphs mechanism to capture the global information of HINs, the node-level sampling and aggregating mechanism to sample and incorporate features from immediate and mediate neighbors of nodes, and the semantic-level aggregating mechanism to combine embeddings with respect to different meta-paths. Extensive experiments on three real-world heterogeneous networks of different types and scales for multiple tasks show that the proposed framework significantly outperforms the baselines. Moreover, HGSAGE has important application values in this research field.

A selection-pattern-aware recommendation model with colored-motif attention network

Heterogeneous information networks (HIN) based models are now widely used to fuse auxiliary information for personalized recommendation. However, existing works mainly focus on capturing high-order connections between heterogeneous nodes through predefined pattern (such as meta-path, etc.), which depends on relevant domain knowledge and ignores the statistical characteristics of the substructures in HIN. How to capture meaningful personalized behavior patterns, especially the selection patterns (i.e. how a user select an item), and incorporate them into the preference model is still a challenge in the research of HIN-based recommendation. Therefore, in this paper, a specifically-designed Colored-Motif Attention Network (CMoAN) is proposed to deal with this problem. In the proposed CMoAN, colored motif are used as the elemental building blocks to represent context-based selection patterns, and then by constructing a motif-based adjacency matrix to capture higher-order semantic association between nodes in HIN. Besides, an attentive graph neural network is designed to efficiently model the semantics and high-order relations information. Extensive experiments on three real-world datasets demonstrate that CMoAN consistently outperforms state-of-the-art methods. Furthermore, experimental results also verify the effectiveness of using colored motif for capturing users’selection patterns.

An Attention-Based Adversarial Disentangle Heterogeneous Embedding for Improving Node Classification

For many years, within the information network analysis and mining (INAM) area, heterogeneous information network (HIN) is considered as a hot topic which have attracted attentions from many researchers. In fact, HIN is considered as the sufficient reflection of real-world information networks which contains multiple types of nodes and links (e.g., knowledge graph, social networks, etc.). Within HIN-based analysis and mining domain, meta-path is considered as the key concept which is employed to extract rich contextual information from semantic relationships, in forms of meta-paths, between multi-typed nodes. However, the various semantic informative sources which are obtained from different meta-paths between nodes are frequently considered as ambiguous and entangled for primitive tasks in INAM area such as node classification, clustering or link prediction. Recently, there are several well-known studies which have dedicated on the disentangling the meta-path-based representations of nodes in HIN through different techniques such as variational embedding and adversarial learning paradigms. However, the disentangled meta-path-based node representation learning in HIN still have several unresolved problems. One of the most crucial limitations of this research direction is related to the capability of properly interpretation for meta-path-varied disentangled node embeddings in order to achieve better fine-tuned performance for different downstream tasks, such as node classification. In order to overcome this problem, in this paper we proposed an improvement of disentangled HIN representation learning for node classification task with the employment of attention-based mechanism, called as: HAttDE model. A self-attention-based mechanism is placed between the encoding and generating components in order to softly align and interpret multiple fused meta-path-based node representations into better classification-friendly embedding spaces. Extensive experiments in benchmark networked datasets demonstrated the effectiveness of our proposed model in comparing with recent state-of-the-art HIN embedding baselines.

Predicting miRNA-disease association from heterogeneous information network with GraRep embedding model

In recent years, accumulating evidences have shown that microRNA (miRNA) plays an important role in the exploration and treatment of diseases, so detection of the associations between miRNA and disease has been drawn more and more attentions. However, traditional experimental methods have the limitations of high cost and time- consuming, a computational method can help us more systematically and effectively predict the potential miRNA-disease associations. In this work, we proposed a novel network embedding-based heterogeneous information integration method to predict miRNA-disease associations. More specifically, a heterogeneous information network is constructed by combining the known associations among lncRNA, drug, protein, disease, and miRNA. After that, the network embedding method Learning Graph Representations with Global Structural Information (GraRep) is employed to learn embeddings of nodes in heterogeneous information network. In this way, the embedding representations of miRNA and disease are integrated with the attribute information of miRNA and disease (e.g. miRNA sequence information and disease semantic similarity) to represent miRNA-disease association pairs. Finally, the Random Forest (RF) classifier is used for predicting potential miRNA-disease associations. Under the 5-fold cross validation, our method obtained 85.11% prediction accuracy with 80.41% sensitivity at the AUC of 91.25%. In addition, in case studies of three major Human diseases, 45 (Colon Neoplasms), 42 (Breast Neoplasms) and 44 (Esophageal Neoplasms) of top-50 predicted miRNAs are respectively verified by other miRNA-disease association databases. In conclusion, the experimental results suggest that our method can be a powerful and useful tool for predicting potential miRNA-disease associations.

Classification by multi-semantic meta path and active weight learning in heterogeneous information networks

Heterogeneous information network (HIN) is a kind of large-scale network which contains different types of objects and complex links. It is distinguished from a homogenous network for its heterogeneity of objects represented as nodes and complexity of links, which also makes the object classification more difficult. A meta-path can denote the relationship between nodes in HINs, and the path information can be enriched by extracting jump-paths. Based on this idea, the problem of data sparseness can be alleviated effectively. As multiple meta-paths represent different semantics, we propose an active weight learning method for each type, which aims to maximize the weight of meta-path with strong correlation and lower the weight if the correlation is weak. The feature matrix based on the meta-path is constructed and the Random Forest classifier is trained to implement the node classification in HINs. The experimental results show that our method achieves better performance in the complex network by using the fewer labeled data. The active learning strategy is effective for identifying objects to label for training.