Heterogeneous Information Network Embedding Research Articles

Meta-path and hypergraph fused distillation framework for heterogeneous information networks embedding

Heterogeneous Information Networks (HINs) are crucial in various intelligent systems. The latest advancements in HIN learning aim to combine meta-paths and hypergraphs, capitalizing on their strengths for further success. However, existing methods typically transform meta-paths into hypergraphs by simply removing the original edges from the meta-paths to integrate two semantics. This will inevitably encounter semantic ambiguity, a so-called semantic-shift problem, during the “meta-path → hyperedges” transforming, causing limited improvements. To address this, we introduce a novel fusion framework that distills knowledge from meta-paths into hypergraphs, mitigating such a problem. Specifically, we propose a unique hyperedge extraction method for incorporating various meta-paths instead of relying solely on one type of meta-path. Subsequently, we introduce a shallow student model to capture high-order information from the hypergraph, complementing a teacher model that focuses on encoding low-order information from meta-paths. Then, a distillation framework is employed to integrate explicitly multi-order information into the student. Experimental results across diverse datasets demonstrate a substantial improvement in node classification tasks, with an average accuracy increase of 2.1% over existing state-of-the-art methods.

An Adaptive Semantic Mining Framework for Heterogeneous Information Network Embedding

Heterogeneous information network (HIN) embedding aims to map heterogeneous nodes to the low-dimensional vector space. The existing embedding models cannot determine the optimal length of semantics automatically and reveal full semantic information adaptively for different heterogeneous networks. To address this challenge, an HIN embedding model with adaptive semantic mining is proposed. First, we project heterogeneous nodes into the same space and aggregate the features of target types in the first-degree range. Then, the semantics of different node types is combined through the attention mechanism, and latent meta-paths are mined using the attention coefficients. Finally, multiple feature aggregation layers are stacked with residual blocks. The residual weights control the proportion of semantics transferred between layers to aggregate more distal features selectively. In addition, we designed the Selected DropLink unit to remove links which transfer negative information, which can further improve the resistance of model to over-smoothing. Experiments show that our model can obtain more accurate embedding results and can automatically mine complex semantic connections between heterogeneous nodes without prior definition of meta-path and semantic depth.

A data fusion framework based on heterogeneous information network embedding for trigger-action programming in IoT

A data fusion framework based on heterogeneous information network embedding for trigger-action programming in IoT

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.

How to select crowdsourcing teams with limited information? A heterogeneous information network embedding approach

How to select crowdsourcing teams with limited information? A heterogeneous information network embedding approach

Source-Aware Embedding Training on Heterogeneous Information Networks

ABSTRACT Heterogeneous information networks (HINs) have been extensively applied to real-world tasks, such as recommendation systems, social networks, and citation networks. While existing HIN representation learning methods can effectively learn the semantic and structural features in the network, little awareness was given to the distribution discrepancy of subgraphs within a single HIN. However, we find that ignoring such distribution discrepancy among subgraphs from multiple sources would hinder the effectiveness of graph embedding learning algorithms. This motivates us to propose SUMSHINE (Scalable Unsupervised Multi-Source Heterogeneous Information Network Embedding)—a scalable unsupervised framework to align the embedding distributions among multiple sources of an HIN. Experimental results on real-world datasets in a variety of downstream tasks validate the performance of our method over the state-of-the-art heterogeneous information network embedding algorithms.

NRWalk2Vec-HIN: spammer group detection based on heterogeneous information network embedding over social media

NRWalk2Vec-HIN: spammer group detection based on heterogeneous information network embedding over social media

Multi-view computable online learner modeling by heterogeneous network: An AI-enabled perspective

To support accurate personalized online intelligent education applications in the big data environment, computable online learner modeling is very important. It can accurately capture the unique needs and characteristics of learners in a computable way and provides customized learning experiences. We propose a structured semantic embedding-based approach for online learner modeling, which considers both the multidimensional learners' characteristics and heterogeneous information on the learning context to develop a comprehensive learner model. Specifically, an AI-enabled heterogeneous information network embedding technique is employed to encode complications of different learners' characteristics and rich relationships between them, which integrate different explicit characteristic information and implicit association information about inherent multidimensional interactions for computable learners modeling. Extensive experimental results and evaluations demonstrate that the proposed model achieves the optimal performance on the Precision@N and Recall@N metrics in course recommendation tasks, and the Accuracy and F−score metrics in learner clustering tasks are also better than other baseline models, which prove the ability to boost the performance of downstream tasks by incorporating and capturing the underlying semantic and structural information among different types of nodes to fuse the embedding for learner modeling.

HeteEdgeWalk: A Heterogeneous Edge Memory Random Walk for Heterogeneous Information Network Embedding.

Most Heterogeneous Information Network (HIN) embedding methods use meta-paths to guide random walks to sample from HIN and perform representation learning in order to overcome the bias of traditional random walks that are more biased towards high-order nodes. Their performance depends on the suitability of the generated meta-paths for the current HIN. The definition of meta-paths requires domain expertise, which makes the results overly dependent on the meta-paths. Moreover, it is difficult to represent the structure of complex HIN with a single meta-path. In a meta-path guided random walk, some of the heterogeneous structures (e.g., node type(s)) are not among the node types specified by the meta-path, making this heterogeneous information ignored. In this paper, HeteEdgeWalk, a solution method that does not involve meta-paths, is proposed. We design a dynamically adjusted bidirectional edge-sampling walk strategy. Specifically, edge sampling and the storage of recently selected edge types are used to better sample the network structure in a more balanced and comprehensive way. Finally, node classification and clustering experiments are performed on four real HINs with in-depth analysis. The results show a maximum performance improvement of 2% in node classification and at least 0.6% in clustering compared to baselines. This demonstrates the superiority of the method to effectively capture semantic information from HINs.

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.

HINChip: Heterogeneous Information Network Representation with Community Hierarchy Preserving

Heterogeneous information network (HIN) representation aims to learn low-dimensional representations for multiple types of nodes and edges while preserving rich structural and semantic information. Existing HIN embedding methods mainly utilize meta-path, meta-graph, and network schema to guide the learned representations. However, these methods may ignore the community hierarchy information in the information network, which is commonly available in many applications. In this paper, we propose HINChip, a new approach for learning representations of Heterogeneous Information Networks with Community Hierarchy Preserving, including intra-community structures and inter-community relationships. Specifically, multiple homogeneous sub-networks are first constructed based on multiple semantics. Second, hierarchical networks are built from fine to coarse on each homogeneous sub-network, preserving intra-community structures and inter-community relationships on the corresponding semantics. The nodes learn the corresponding vectors from each level of the hierarchical networks by the existing representation learning method. Third, multi-level-integrated semantic attention is proposed to combine the node vectors of each level and weigh the importance of different homogeneous sub-networks under given semantics. The proposed HINChip considers only the topology of HIN and outperforms methods trained with node attributes and labels in the experiments conducted on four real-world datasets.

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.

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.

MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms.

Discovering the drug-target interactions (DTIs) is a crucial step in drug development such as the identification of drug side effects and drug repositioning. Since identifying DTIs by web-biological experiments is time-consuming and costly, many computational-based approaches have been proposed and have become an efficient manner to infer the potential interactions. Although extensive effort is invested to solve this task, the prediction accuracy still needs to be improved. More especially, heterogeneous network-based approaches do not fully consider the complex structure and rich semantic information in these heterogeneous networks. Therefore, it is still a challenge to predict DTIs efficiently. In this study, we develop a novel method via Multiview heterogeneous information network embedding with Hierarchical Attention mechanisms to discover potential Drug-Target Interactions (MHADTI). Firstly, MHADTI constructs different similarity networks for drugs and targets by utilizing their multisource information. Combined with the known DTI network, three drug-target heterogeneous information networks (HINs) with different views are established. Secondly, MHADTI learns embeddings of drugs and targets from multiview HINs with hierarchical attention mechanisms, which include the node-level, semantic-level and graph-level attentions. Lastly, MHADTI employs the multilayer perceptron to predict DTIs with the learned deep feature representations. The hierarchical attention mechanisms could fully consider the importance of nodes, meta-paths and graphs in learning the feature representations of drugs and targets, which makes their embeddings more comprehensively. Extensive experimental results demonstrate that MHADTI performs better than other SOTA prediction models. Moreover, analysis of prediction results for some interested drugs and targets further indicates that MHADTI has advantages in discovering DTIs. https://github.com/pxystudy/MHADTI.

Meta-HGT: Metapath-aware HyperGraph Transformer for heterogeneous information network embedding

Heterogeneous information network embedding aims to learn low-dimensional node vectors in heterogeneous information networks (HINs), concerning not only structural information but also heterogeneity of diverse node and relation types. Most existing HIN embedding models mainly rely on metapath to define composite relations between node pairs and thus extract substructures from the original HIN. However, due to the pairwise structure of metapath, these models fail to capture the high-order relations (such as “Multiple authors co-authoring a paper”) implicitly contained in HINs. To tackle the limitation, this paper proposes a Metapath-aware HyperGraph Transformer (Meta-HGT) for node embedding in HINs. Meta-HGT first extends metapath to guide the high-order relation extraction from original HIN and constructs multiple metapath based hypergraphs with diverse composite semantics. Then, Meta-HGT learns the latent node and hyperedge embeddings in each metapath based hypergraph through Meta-HGT layers. Each layer consists of two types of components, i.e., intra-hyperedge aggregation and inter-hyperedge aggregation, in which a novel type-dependent attention mechanism is proposed for node and hyperedge feature aggregation. Finally, it fuses multiple node embeddings learned from different metapath based hypergraphs via a semantic attention layer and generates the final node embeddings. Extensive experiments have been conducted on three HIN benchmarks for node classification. The results demonstrate that Meta-HGT achieves state-of-the-art performance on all three datasets.

Hierarchical User Intention-Preference for Sequential Recommendation with Relation-Aware Heterogeneous Information Network Embedding.

Existing recommender systems usually make recommendations by exploiting the binary relationship between users and items, and assume that users only have flat preferences for items. They ignore the users' intentions as an origin and driving force for users' performance. Cognitive science tells us that users' preference comes from an explicit intention. They first have an intention to possess a particular (type of) item(s) and then their preferences emerge when facing multiple available options. Most of the data used in recommender systems are composed of heterogeneous information contained in a complicated network's structure. Learning effective representations from these heterogeneous information networks (HINs) can help capture the user's intention and preferences, therefore, improving recommendation performance. We propose a hierarchical user's intention and preferences modeling for sequential recommendation based on relation-aware HIN embedding (HIP-RHINE). We first construct a multirelational semantic space of heterogeneous information networks to learn node embedding based on specific relations. We then model user's intention and preferences using hierarchical trees. Finally, we leverage the structured decision patterns to learn user's preferences and thereafter make recommendations. To demonstrate the effectiveness of our proposed model, we also report on the conducted experiments on three real data sets. The results demonstrated that our model achieves significant improvements in Recall and Mean Reciprocal Rank metrics compared with other baselines.

MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding

Depending on the type of information network, information network embedding is classified into homogeneous information network embedding and heterogeneous information network (HIN) embedding. Compared with the homogeneous network, HIN composition is more complex and contains richer semantics. At present, the research on homogeneous information network embedding is relatively mature. However, if the homogeneous information network model is directly applied to HIN, it will cause incomplete information extraction. It is necessary to build a specialized embedding model for HIN. Learning information network embedding based on the meta-path is an effective approach to extracting semantic information. Nevertheless, extracting HIN embedding only from a single view will cause information loss. To solve these problems, we propose a multi-view fusion-based HIN embedding model, called MFHE. MFHE includes four parts: node feature space transformation, subview information extraction, multi-view information fusion, and training. MFHE divides HIN into different subviews based on meta-paths, models the local information accurately in the subviews based on the multi-head attention mechanism, and then fuses subview information through a spatial matrix. In this paper, we consider the relationship between subviews; thus, the MFHE is applicable to complex HIN embedding. Experiments are conducted on ACM and DBLP datasets. Compared with baselines, the experimental results demonstrate that the effectiveness of MFHE and HIN embedding has been improved.

CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling.

Heterogeneous information network (HIN) embedding is an important tool for tasks such as node classification, community detection, and recommendation. It aims to find the representations of nodes that preserve the proximity between entities of different nature. A family of approaches that are widely adopted applies random walk to generate a sequence of heterogeneous contexts, from which, the embedding is learned. However, due to the multipartite graph structure of HIN, hub nodes tend to be over-represented to their context in the sampled sequence, giving rise to imbalanced samples of the network. Here, we propose a new embedding method: CoarSAS2hvec. The self-avoiding short sequence sampling with the HIN coarsening procedure (CoarSAS) is utilized to better collect the rich information in HIN. An optimized loss function is used to improve the performance of the HIN structure embedding. CoarSAS2hvec outperforms nine other methods in node classification and community detection on four real-world data sets. Using entropy as a measure of the amount of information, we confirm that CoarSAS catches richer information of the network compared with that through other methods. Hence, the traditional loss function applied to samples by CoarSAS can also yield improved results. Our work addresses a limitation of the random-walk-based HIN embedding that has not been emphasized before, which can shed light on a range of problems in HIN analyses.

Heterogeneous information network embedding for user behavior analysis on social media

Heterogeneous information network embedding for user behavior analysis on social media

Heterogeneous information network embedding with incomplete multi-view fusion

Heterogeneous information network embedding with incomplete multi-view fusion