Heterogeneous Information Network Research Articles

Meta-path automatically extracted from heterogeneous information network for recommendation

Meta-path automatically extracted from heterogeneous information network for recommendation

Analyzing Heterogeneous Networks With Missing Attributes by Unsupervised Contrastive Learning.

Heterogeneous information networks (HINs) are potent models of complex systems. In practice, many nodes in an HIN have their attributes unspecified, resulting in significant performance degradation for supervised and unsupervised representation learning. We developed an unsupervised heterogeneous graph contrastive learning approach for analyzing HINs with missing attributes (HGCA). HGCA adopts a contrastive learning strategy to unify attribute completion and representation learning in an unsupervised heterogeneous framework. To deal with a large number of missing attributes and the absence of labels in unsupervised scenarios, we proposed an augmented network to capture the semantic relations between nodes and attributes to achieve a fine-grained attribute completion. Extensive experiments on three large real-world HINs demonstrated the superiority of HGCA over several state-of-the-art methods. The results also showed that the complemented attributes by HGCA can improve the performance of existing HIN models.

Hawk: Rapid Android Malware Detection Through Heterogeneous Graph Attention Networks.

Android is undergoing unprecedented malicious threats daily, but the existing methods for malware detection often fail to cope with evolving camouflage in malware. To address this issue, we present Hawk, a new malware detection framework for evolutionary Android applications. We model Android entities and behavioral relationships as a heterogeneous information network (HIN), exploiting its rich semantic meta-structures for specifying implicit higher order relationships. An incremental learning model is created to handle the applications that manifest dynamically, without the need for reconstructing the whole HIN and the subsequent embedding model. The model can pinpoint rapidly the proximity between a new application and existing in-sample applications and aggregate their numerical embeddings under various semantics. Our experiments examine more than 80 860 malicious and 100 375 benign applications developed over a period of seven years, showing that Hawk achieves the highest detection accuracy against baselines and takes only 3.5 ms on average to detect an out-of-sample application, with the accelerated training time of 50× faster than the existing approach.

Interpretable answer retrieval based on heterogeneous network embedding

Community question answering is a rising technology based on users' autonomous interactive behaviors, such as posting their issues, answering questions based on their experience, and commenting on existing questions. As a result of its use of natural language for communication and stimulation of user interest in information sharing, it has increasingly taken the place of other channels as the main way that people learn new things. Multi-type entity characteristics fusion and poor answer interpretability are the two major concerns that currently plague community answer prediction research. The Interpretable Answer Retrieval Method Based on Heterogeneous Network Embedding (IARHNE) is what we present in this work. It combines complex entity features and generates interpretable predicted answers. In order to incorporate the interactions of several kinds of individuals in answer social retrieval, we first build a heterogeneity graph. In order to acquire entity embeddings, we secondly use the heterogeneous graph neural network. We then adopt the vector distance to convert the entity matching problem in the heterogeneous information network into a homogeneous node similarity job. Finally, using entity correlation to predict answers, we provide a list of answers to the new query and interpret them using meta-paths. Comparative studies using three authentic datasets demonstrate the benefits of IARHNE for interpretative question-answering research.

Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-Experts

The rise of Massive Open Online Courses (MOOCs) has increased the large audience for higher education. Different learners face different learning difficulties in the process of online learning. In order to ensure the quality of teaching, online learning resource recommendation services should be more personalised and have more choices. In this paper, we propose a joint recommendation algorithm for knowledge concepts and learning partners based on improved MMoE (Multi-gate Mixture-of-Experts). Firstly, the heterogeneous information network (HIN) is constructed based on the MOOC platform and appropriate meta-paths are selected in order to extract the human–computer interaction information and student–student interaction information generated during the learners’ online learning processes more completely. Secondly, the temporal behavioural characteristics of students are obtained based on their learning paths as well as their knowledge of conceptual characteristics, and LSTM (Long Short-Term Memory) is used to mine students’ current learning interests. Finally, the gating network in MMoE is changed into an attention mechanism network, and for different tasks, multiple attention mechanism networks are used to fuse the learner’s human–computer interaction information, student–student interaction information, and interest characteristics to generate learner representations that are more in line with the respective task and to complete the tasks of knowledge conception and learning partner recommendation. Experiments on publicly available MOOC datasets show that the method proposed in this paper provides more accurate and varied personalization services to online learners compared to the latest proposed methods.

CR-LCRP: Course recommendation based on Learner–Course Relation Prediction with data augmentation in a heterogeneous view

Course recommendation aims to offer suitable courses for learners and alleviate dropout issue in Massive Open Online Course (MOOC) learning scenarios. The present studies focus on learning semantic representations by modeling the learner’s personal information and the courses’ teaching attributes. However, with massive courses offered and various learners involved, the data sparsity and cold start are still two dominant challenges hindering the booming of online learning, which roughly results from the scarce exploitation on various relationships between learners and courses. To alleviate the above issues, we leverage the strong ability of Heterogeneous Information Network (HIN) in modeling heterogeneous data types and complex structural characteristics, and propose a Course Recommendation method based on Learner–Course Relation Prediction (CR-LCRP) with multi-granularity data augmentation strategy. Specifically, the multi-source interactive data on courses, the learner–learner similarity and the course–course similarity are integrated in building a heterogeneous information network. With both explicit and implicit features extracted and structural information from meta-paths enhanced, the CR-LCRP method learns high-quality representations of learners and courses, and eventually performs learner–course relation prediction in MOOC learning scenarios. Finally, extensive experiments are conducted on real-world MOOCs datasets and the experimental results demonstrate that the proposed CR-LCRP method outperforms the state-of-the-art baseline models with superior performance on several widely used evaluation metrics.

Counterfactual Explanation for Fairness in Recommendation

Fairness-aware recommendation alleviates discrimination issues to build trustworthy recommendation systems. Explaining the causes of unfair recommendations is critical, as it promotes fairness diagnostics, and thus secures users’ trust in recommendation models. Existing fairness explanation methods suffer high computation burdens due to the large-scale search space and the greedy nature of the explanation search process. Besides, they perform feature-level optimizations with continuous values, which are not applicable to discrete attributes such as gender and age. In this work, we adopt counterfactual explanations from causal inference and propose to generate attribute-level counterfactual explanations, adapting to discrete attributes in recommendation models. We use real-world attributes from Heterogeneous Information Networks (HINs) to empower counterfactual reasoning on discrete attributes. We propose a Counterfactual Explanation for Fairness (CFairER) that generates attribute-level counterfactual explanations from HINs for item exposure fairness. Our CFairER conducts off-policy reinforcement learning to seek high-quality counterfactual explanations, with attentive action pruning reducing the search space of candidate counterfactuals. The counterfactual explanations help to provide rational and proximate explanations for model fairness, while the attentive action pruning narrows the search space of attributes. Extensive experiments demonstrate our proposed model can generate faithful explanations while maintaining favorable recommendation performance.

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.

C-privacy: a social relationship-driven image customization sharing method in cyber-physical networks

Cyber-Physical Networks(CPN) are comprehensive systems that integrate information and physical domains, and are widely used in various fields such as online social networking, smart grids, and the Internet of Vehicles(IoV). With the increasing popularity of digital photography and Internet technology, more and more users are sharing images on CPN. However, many images are shared without any privacy processing, exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI) algorithms. Existing image sharing methods lack fine-grained image sharing policies and cannot protect user privacy. To address this issue, we propose a social relationship-driven privacy customization protection model for publishers and co-photographers. We construct a heterogeneous social information network centered on social relationships, introduce a user intimacy evaluation method with time decay, and evaluate privacy levels considering user interest similarity. To protect user privacy while maintaining image appreciation, we design a lightweight face-swapping algorithm based on Generative Adversarial Network(GAN) to swap faces that need to be protected. Our proposed method minimizes the loss of image utility while satisfying privacy requirements, as shown by extensive theoretical and simulation analyses.

Collusive spam detection from Chinese community question answering sites: A collective classification framework

With Community Question Answering (CQA) sites evolving into quite popular knowledge-sharing platforms on the Internet, they have also become ideal places for various spammers to spread fake or promotional information. Recently, with the rapid development of crowdsourcing systems, numerous malicious users have launched organized spam campaigns, conducting many spam accounts to carry out collusive spamming activities on CQA sites. In these campaigns, the spammers do not act independently but post deceptive questions and answers (Q&As) collaboratively, which makes the Q&As closely related to each other, but the spam clues of them are even less visible. Therefore, most existing spam detection works may fail to detect these carefully organized and posted collusive CQA spam. In this paper, taking Baidu Zhidao, a popular CQA platform in Chinese, as the study object, we propose a Collective Classification framework for community Question Answering spam detection (CCQA), which collectively identifies the collusive CQA spam using Q&A features and the correlations among Q&As. First, we define the Deceptive Pattern of Q&As, based on which the real Q&A groups are extracted. Then, we extract several highly discriminative Q&A features from both individual and group levels, and propose several types of correlations, which correlate the Q&As that are more likely to have the same labels. After uniformly modeling the Q&As, features, and correlations in the Attributed Heterogeneous Information Network (AHIN), a semi-supervised collective classification algorithm is proposed to detect the collusive Q&A spam. Experimental results on a real-life dataset demonstrate that CCQA can accurately detect the collusive CQA spam, and outperform a number of competitive baselines.

Mashup-oriented API recommendation via pre-trained heterogeneous information networks

Combining different Web APIs to create Mashups has become very popular nowadays. Choosing suitable ones from massive Web APIs is of vital importance for efficient Mashup creations. A number of Mashup-oriented API recommendation methods have been proposed to address this issue, but they have limitations in their ability to exploit the rich attributes and connection data of Web APIs, which impedes their performance. By modeling the API-related data as a heterogeneous information network and using pre-training technology, this paper proposes an accurate API recommendation method, named PHRec. In this method, the meta paths of APIs in the heterogeneous information network are exploited to obtain their context semantics; the method adopts an attention mechanism. Extensive experiments have been conducted with a real Web API dataset to evaluate the proposed method. The experimental results demonstrate that it significantly outperforms the state-of-the-art methods in the Web API recommendation task.

Heterogeneous network influence maximization algorithm based on multi-scale propagation strength and repulsive force of propagation field

Heterogeneous networks, like social and academic networks are widespread in the real world, characterized by diverse nodes and complex relationships. Influence maximization is a crucial research topic, in these networks, as it can help in comprehending the mechanisms of information propagation and diffusion. Effectively utilizing complex structural information poses a challenge in current research on influence maximization in heterogeneous information networks. As a solution to this problem, a heterogeneous network influence maximization algorithm based on the multi-scale propagation strength and repulsive force of propagation field is proposed. Firstly, based on the propagation field, we design a multi-scale propagation strength index for the propagation ability of nodes to achieve maximum coverage of influence propagation. Specifically, in the homogeneous structure, the homogeneous propagation strength describes the propagation ability of nodes. In the heterogeneous structure, the heterogeneous shallow propagation strength and the heterogeneous deep propagation strength are designed to exploit the local and global spreading ability of nodes using meta-paths and link prediction based on graph neural networks, respectively. Secondly, to ensure the minimum overlap in the propagation range of seed nodes, we designed the overlapping repulsive force between node pairs in the propagation field. Finally, considering the complexity of the propagation process of heterogeneous information networks, an independent cascade model based on meta-paths is proposed. Based on experiments conducted with several datasets, our algorithm outperforms baseline algorithms for solving influence maximization problem.

ASSL-HGAT: Active semi-supervised learning empowered heterogeneous graph attention network

Recently, heterogeneous graph attention network (HGAT) has been widely applied to various machine learning tasks and achieved remarkable results with sufficient labeled data. However, it is noteworthy that in many tasks, labeled data is scarce and the data labeling process is expensive. To that end, this paper presents a novel framework for learning from data with limited labels by organically integrating active learning (AL) and semi-supervised learning (SSL) into heterogeneous graph network. Our framework consists of three components: heterogeneous information network (HIN), HGAT and active semi-supervised learning strategy (ASSL). The adjustable HIN is first constructed by fusing multi-modal features. Then the HGAT is used to encode HIN and the double-layer attention mechanism can mitigate noises during information fusion. The ASSL is further designed to enrich the high-quality labeled training data for model train. Samples selected by uncertainty-aware AL and samples labeled by pseudo-label selection based SSL are finally mixed to iteratively train the ASSL-HGAT framework. Experimental results prove that ASSL-HGAT surpasses the compared state-of-the-art methods on five representative semi-supervised benchmark datasets under the same experimental settings.

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.

A Heterogeneous Graph Neural Network With Attribute Enhancement and Structure-Aware Attention

Heterogeneous information network (HIN) has been applied in a wide variety of graph analysis tasks. At present, it is a trend of heterogeneous graph neural networks (HGNNs) to cast the meta-paths aside, since it solves the problem of structural information loss caused by artificially designed meta-paths. However, existing meta-path-free HGNNs fail to take into account that most node types in many HINs have no attributes, and they cannot make full use of sparse node attributes when applied to HINs with missing attributes. Furthermore, their computation of attention coefficients explores the correlations of node attributes while almost ignoring structural ones, which may limit the expression ability of the model and cause overfitting in model training. To alleviate these issues, we propose an HGNN with attribute enhancement and structure-aware attention (HGNN-AESA). First, we design an attribute enhancement module (AEM) to connect more useful attributed nodes to the target nodes. Specifically, AEM introduces a random walk with restart (RWR) strategy to obtain structural relevance scores of each node within its specific subgraph. The structural relevance scores are used to capture potentially influential attributed nodes in high-order neighborhood for each target node. Second, we propose heterogeneous structure-aware attention layers (HSALs) to learn node representations. HSALs follow a hierarchical attention framework, including node-level and type-level attention. The node-level attention aggregates feature (attribute) embeddings of same-type neighbors, and the relevant attention coefficients depend on the combination of node attributes and heterogeneous structural interventions. The type-level attention fuses all type-specific vector representations and generates the ultimate node embedding. Finally, extensive experiments on three different real-world HIN datasets demonstrate that our model outperforms 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.

StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information Networks

Similarity measures in heterogeneous information networks (HINs) have become increasingly important in recent years. Most measures in such networks are based on the meta path, a relation sequence connecting object types. However, in real-world scenarios, there exist many complex semantic relationships, which cannot be captured by the meta path. Therefore, a meta structure is proposed, which is a directed acyclic graph of object and relation types. In this paper, we explore the complex semantic meanings in HINs and propose a meta-structure-based similarity measure called StructSim. StructSim models the probability of subgraph expansion with bias from source node to target node. Different from existing methods, StructSim claims that the subgraph expansion is biased, i.e., the probability may be different when expanding from the same node to different nodes with the same type based on the meta structure. Moreover, StructSim defines the expansion bias by considering two types of node information, including out-neighbors of current expanded nodes and in-neighbors of next hop nodes to be expanded. To facilitate the implementation of StructSim, we further designed the node composition operator and expansion probability matrix with bias. Extensive experiments on DBLP and YAGO datasets demonstrate that StructSim is more effective than the state-of-the-art approaches.

Geography-aware Heterogeneous Graph Contrastive Learning for Travel Recommendation

Recommendation system concentrates on quickly matching products to consumer’s needs which plays a major role in improving user experiences and increase conversion rate. Travel recommendation has become a hot topic in both industry and academia with the development of the tourism industry. Nevertheless, the selection of travel products entails careful consideration of various geographical factors, such as departure and destination. Meanwhile, due to the limitation of finance and time, users browse and purchase travel products less frequently than they do for traditional products, which leads to data sparsity problem in representation learning. To solve these challenges, a novel model named GHGCL (short for G eography-aware H eterogeneous G raph C ontrastive L earning) is proposed for recommending travel products. Concretely, we model the travel recommender system as an heterogeneous information network with geographical information, and capture diverse user preferences from local and high-order structures. Especially, we design two kinds of contrastive learning tasks for better user and travel product representation learning. The multi-view contrastive learning aims to bridge the gap between network schema and meta-path view representations. The meta-path contrastive learning focuses on modeling the coarse-grained commonality between different meta-paths from the perspective of different geographical factors, i.e., departure and destination. We assess the performance of GHGCL by performing a series of experiments on a real-world dataset and the results clearly verify its superiority as compared to the baseline methods.

Efficient and effective [formula omitted]-core-based community search over attributed heterogeneous information networks

Given a heterogeneous information network (HIN) G and a query node q, community search (CS) over an HIN identifies a cohesive subgraph from G that contains q. Although HINs with attributes on nodes (called AHINs) are prevalent today, the CS over AHINs (CS-AHIN) is ignored in the literature. Though we can convert an AHIN to an attributed homogeneous graph given a meta-path, then apply the CS approaches for attributed homogeneous graphs to solve CS-AHIN, it is problematic for two reasons. (1) Complete graph conversion is time-consuming and unnecessary, because the search only involves the query node's neighborhood, not the entire graph. (2) Existing attribute cohesiveness metrics are not strict enough to reflect substantial similarities among the community's pairwise nodes. To resolve this, we define the CS-AHIN problem atop a strict attribute cohesiveness metric that supports textual and numerical attributes simultaneously. We show the problem is NP-hard. To address it, we propose an exact baseline to return the global optimal result. Then, we propose three heuristic algorithms using a general greedy search framework to speed up the efficiency. Moreover, we present a cohesiveness-aware proximity graph-based index to boost the performance. Comprehensive experimental studies on various real-world datasets demonstrate our method's superiority.

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.