Heterogeneous Network Embedding Method Research Articles

Temporal Heterogeneous Information Network Embedding via Semantic Evolution

Real-world networks are often heterogeneous and constantly changing over time. Evolution reveals the trend of network development, which is vital for predicting its future state, and network embedding can effectively learn the information from it. Nevertheless, previous works only consider the impact of meta-path instances or node neighbors on the network dynamics but ignore the relationship between them, and hence the hidden semantic information is missed, which will result in performance deterioration. Therefore, we propose a novel temporal heterogeneous information network embedding method (SemE), which abstracts the instance of the meta-path as semantic units and then considers the interaction between them to discover deeper semantic information. Specifically, we first construct semantic networks by the Ethernet topology and the interaction between semantic units. The semantic units are sampled based on a pre-designed meta-path-guided random walk. To further capture the semantic evolution of the semantic network, we learn the embedding of nodes by the attention-Hawkes process. Finally, we generate the final embedding by aggregating the structure, semantic and temporal information with the attention mechanism. Experiments on three real-world temporal heterogeneous information networks show that our model performs better than competitive counterparts.

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.

Meta-path fusion based neural recommendation in heterogeneous information networks

As a powerful data modeling tool, Heterogeneous Information Network (HIN) has been successfully used in auxiliary information exploitation to boost recommendation performance. For HIN based recommendation, it is challenging to extract and fuse useful features of user preferences and item attributes under different semantic paths in HINs. Existing methods leverage a pre-defined fusion function to integrate different semantics for recommendation, which cannot characterize the complex nonlinear interactions between users and items. In this paper, we present a general framework named MNRec, short for Meta-path fusion based Neural Recommendation, to extract and fuse user and item embeddings under different meta-paths for recommendation. Under the framework, we propose an instantiation of MNRec with Multi-Layer Perceptron (MLP) structure. It consists of two major steps, i.e., meta-path based heterogeneous network embedding and deep learning based rating prediction. Concretely, appropriate meta-paths are first designed according to domain knowledge. Then the embeddings of users and items are obtained through a meta-path and commuting matrix based heterogeneous network embedding method. Finally, in light of the powerful nonlinear modeling capabilities of deep neural networks, the learned embeddings under different meta-paths are integrated into a two-pathway MLP structure for rating prediction. Experimental results on three real-world datasets demonstrate the superiority and effectiveness of MNRec compared with state-of-the-art baselines in rating prediction.

Predicting Drug-Target Interactions Over Heterogeneous Information Network.

Identifying Drug-Target Interactions (DTIs) is a critical step in studying pathogenesis and drug development. Due to the fact that conventional experimental methods usually suffer from high costs and low efficiency, various computational methods have been proposed to detect potential DTIs by extracting features from the biological information of drugs and their target proteins. Though effective, most of them fall short of considering the topological structure of the DTI network, which provides a global view to discover novel DTIs. In this paper, a network-based computational method, namely LG-DTI, is proposed to accurately predict DTIs over a heterogeneous information network. For drugs and target proteins, LG-DTI first learns not only their local representations from drug molecular structures and protein sequences, but also their global representations by using a semi-supervised heterogeneous network embedding method. These two kinds of representations consist of the final representations of drugs and target proteins, which are then incorporated into a Random Forest classifier to complete the task of DTI prediction. The performance of LG-DTI has been evaluated on two independent datasets and also compared with several state-of-the-art methods. Experimental results show the superior performance of LG-DTI. Moreover, our case study indicates that LG-DTI can be a valuable tool for identifying novel DTIs.

A Dynamic Heterogeneous Information Network Embedding Method Based on Meta-Path and Improved Rotate Model

Aiming at the current situation of network embedding research focusing on dynamic homogeneous network embedding and static heterogeneous information network embedding but lack of dynamic information utilization, this paper proposes a dynamic heterogeneous information network embedding method based on the meta-path and improved Rotate model; this method first uses meta-paths to model the semantic relationships involved in the heterogeneous information network, then uses GCNs to get local node embedding, and finally uses meta-path-level aggression mechanisms to aggregate local representations of nodes, which can solve the heterogeneous information utilization issues. In addition, a temporal processing component based on a time decay function is designed, which can effectively handle temporal information. The experimental results on two real datasets show that the method has good performance in networks with different characteristics. Compared to current mainstream methods, the accuracy of downstream clustering and node classification tasks can be improved by 0.5~41.8%, which significantly improves the quality of embedding, and it also has a shorter running time than most comparison algorithms.

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

ABSTRACT Predicting interactions between drugs and target proteins has become an essential task in the drug discovery process. Although the method of validation via wet-lab experiments has become available, experimental methods for drug-target interaction (DTI) 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. However, most prediction methods do not consider the topological structures characteristics of the relationship. In this paper, we propose a relational topology-based heterogeneous network embedding method to predict drug-target interactions, abbreviated as RTHNE_ DTI. We first construct a heterogeneous information network based on the interaction between different types of nodes, to enhance the ability of association discovery by fully considering the topology of the network. Then drug and target protein nodes can be represented by the other types of nodes. According to the different topological structure of the relationship between the nodes, we divide the relationship in the heterogeneous network into two categories and model them separately. Extensive experiments on the real-world drug datasets, RTHNE_DTI produces high efficiency and outperforms other state-of-the-art methods. RTHNE_DTI can be further used to predict the interaction between unknown interaction drug-target pairs.

Factor graph-aggregated heterogeneous network embedding for disease-gene association prediction

BackgroundExploring the relationship between disease and gene is of great significance for understanding the pathogenesis of disease and developing corresponding therapeutic measures. The prediction of disease-gene association by computational methods accelerates the process.ResultsMany existing methods cannot fully utilize the multi-dimensional biological entity relationship to predict disease-gene association due to multi-source heterogeneous data. This paper proposes FactorHNE, a factor graph-aggregated heterogeneous network embedding method for disease-gene association prediction, which captures a variety of semantic relationships between the heterogeneous nodes by factorization. It produces different semantic factor graphs and effectively aggregates a variety of semantic relationships, by using end-to-end multi-perspectives loss function to optimize model. Then it produces good nodes embedding to prediction disease-gene association.ConclusionsExperimental verification and analysis show FactorHNE has better performance and scalability than the existing models. It also has good interpretability and can be extended to large-scale biomedical network data analysis.

AttrHIN: Network Representation Learning Method for Heterogeneous Information Network

Network representation learning can map complex network to the low dimensional vector space, capture the topological properties of networks, and reduce the time complexity and space complexity of the algorithm. However, most of the existing network representation learning (NRL) methods are for the homogeneous networks, while the real-world networks are usually heterogeneous, therefore, it is more practical to provide an intelligent insight into the evolution of heterogeneous networks. In this paper, we propose a novel heterogeneous network embedding method, called AttrHIN, which adopts weighted meta-path-based random walks strategy, and can make full use of the attribute information to capture the latent features. AttrHIN is suitable for the different types of nodes in heterogeneous networks. Extensive experimental results show that compared with the state-of-art algorithms, AttrHIN achieves better results in Macro-F1 and Micro-F1 for multi-class node classification and Link Prediction on several real-world datasets.

Visual Sentiment Analysis With Social Relations-Guided Multiattention Networks.

These days, social media users tend to express their feelings through sharing images online. Capturing the emotions embedded in these social images involves great research challenges and practical values. Most existing works concentrate on extracting the visual feature from a global view, while ignoring the fact that visual objects are also rich in emotion. How to leverage the multilevel visual features to improve the sentiment analysis performance is important yet challenging. Besides, existing works view each social image as an independent sample while ignoring the rich correlations among social images, which may be helpful in detecting visual emotion. In this article, we propose a novel model called social relations-guided multiattention networks (SRGMANs) to incorporate both the multilevel (region-level and object-level) visual features of a single image and the correlations among multiple social images to conduct visual sentiment analysis. Specifically, we first construct a heterogeneous network consisting of various types of social relations and introduce a heterogeneous network embedding method to learn the network representation for each image. Then, two visual attention branches (region attention network and object attention network) are devised to extract emotional and discriminative visual features. For each branch, we design a self-attention module to capture the emotional dependencies among visual parts. Besides, a network-guided attention module is also designed in each branch to focus on more network-related emotional visual parts with the guidance of the topology information. Finally, the attended visual features from the two attention models, together with network representation features, are combined within a holistic framework to predict the sentiment of social images. Extensive experiments demonstrate the superiority of our model on three benchmark datasets.

Multi-Task Travel Route Planning With a Flexible Deep Learning Framework

Travel route planning aims to map out a feasible sightseeing itinerary for a traveler covering famous attractions and meeting the tourist's desire. It is very useful for tourists to plan their travel routes when they want to travel at unfamiliar scenic cities. Existing methods for travel route planning mainly concentrate on a single planning problem for a special task, but is not capable of being applied to other tasks. For example, previous must-visit planning methods cannot be applied to the next-point recommendation, despite these two tasks are closely related to each other in travel route planning. Besides, most of the existing work do not consider the important auxiliary information such as Point of Interests (POI) attributes, user preference, and historical route data in their approaches. In this paper, we propose a flexible Multi-task Deep Travel Route Planning framework named MDTRP to integrate rich auxiliary information for more effective planning. Specifically, we first construct a heterogeneous network through the relations between users and POIs and employ a heterogeneous network embedding method to learn the features of users and POIs. Then we present an attention-based deep model to integrate the auxiliary information and focus on important visited points for the prediction of next POIs. Finally, a beam search algorithm is introduced to flexibly generate multiple feasible route candidates for three types of planning tasks (next-point recommendation, general route planning, and must-visit planning). We introduce six public datasets to conduct extensive experiments, of which the results demonstrate the flexibility and superiority of the proposed approach in travel route planning.

Heterogeneous Social Recommendation Model With Network Embedding

Due to the number of users and items increasing sharply, data sparsity has become an extremely serious problem for recommendation systems. Social relations consist of complex and rich information, which have a good alleviation effect on sparsity problems. Heterogeneous Information Network (HIN) is excellent in modeling the complex and structural information. Hence, we integrate HIN into the social recommendation. In this paper, we propose a model named Heterogeneous Social Recommendation model with Network Embedding (HSR). The social relations are divided into direct social relations and indirect social relations. We design a novel social influence calculation method to evaluate the influence of direct social relations. Based on the heterogeneous information network embedding method, we represent indirect social relations as feature embeddings and transform the learned embeddings into user-item feature interaction matrix by outer product. The final item list for a user is generated by the method of the convolutional neural network combined with the list of items generated by direct social relations. Extensive experiments on three real-world datasets show significant improvements of our proposed method over state-of-the-art methods. Additionally, experiments show that using heterogeneous network embedding can obtain better recommendation performance.

Heterogeneous network embedding enabling accurate disease association predictions

BackgroundIt is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation.ResultsWe incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset.ConclusionsWe propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation.

A 2 CMHNE

Network representation learning is playing an important role in network analysis due to its effectiveness in a variety of applications. However, most existing network embedding models focus on homogeneous networks and neglect the diverse properties such as different types of network structures and associated multimedia content information. In this article, we learn node representations for multimodal heterogeneous networks, which contain multiple types of nodes and/or links as well as multimodal content such as texts and images. We propose a novel attention-aware collaborative multimodal heterogeneous network embedding method (A 2 CMHNE), where an attention-based collaborative representation learning approach is proposed to promote the collaboration of structure-based embedding and content-based embedding, and generate the robust node representation by introducing an attention mechanism that enables informative embedding integration. In experiments, we compare our model with existing network embedding models on two real-world datasets. Our method leads to dramatic improvements in performance by 5%, and 9% compared with five state-of-the-art embedding methods on one benchmark (M10 Dataset), and on a multi-modal heterogeneous network dataset (WeChat dataset) for node classification, respectively. Experimental results demonstrate the effectiveness of our proposed method on both node classification and link prediction tasks.