Unsupervised Heterogeneous Graph Neural Networks for One-Class Tasks: Exploring Early Fusion Operators

RA-HGNN: Attribute completion of heterogeneous graph neural networks based on residual attention mechanism

Aspect-based sentiment analysis aims to predict sentiment polarities of given aspects in text. Most current approaches employ attention-based neural methods to capture semantic relationships between aspects and words in one sentence. However, these methods ignore the fact that sentences with the same aspect and sentiment polarity often share the structure and semantic information in a domain, which leads to lower model performance. To mitigate this problem, we propose a heterogeneous aspect graph neural network (HAGNN) to learn the structure and semantic knowledge from intersentence relationships. Our model is a heterogeneous graph neural network since it contains three different kinds of nodes: word nodes, aspect nodes, and sentence nodes. These nodes can pass structure and semantic information between each other and update their embeddings to improve the performance of our model. To the best of our knowledge, we are the first to use a heterogeneous graph to capture relationships between sentences and aspects. The experimental results on five public datasets show the effectiveness of our model outperforming some state-of-the-art models.

Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect the impact of user historical sessions while modeling user preference, which often leads to non-personalized recommendation. Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions. To address these issues, we propose a novel Heterogeneous Global Graph Neural Networks (HG-GNN) to exploit the item transitions over all sessions in a subtle manner for better inferring user preference from the current and historical sessions. To effectively exploit the item transitions over all sessions from users, we propose a novel heterogeneous global graph that contains item transitions of sessions, user-item interactions and global co-occurrence items. Moreover, to capture user preference from sessions comprehensively, we propose to learn two levels of user representations from the global graph via two graph augmented preference encoders. Specifically, we design a novel heterogeneous graph neural network (HGNN) on the heterogeneous global graph to learn the long-term user preference and item representations with rich semantics. Based on the HGNN, we propose the Current Preference Encoder and the Historical Preference Encoder to capture the different levels of user preference from the current and historical sessions, respectively. To achieve personalized recommendation, we integrate the representations of the user current preference and historical interests to generate the final user preference representation. Extensive experimental results on three real-world datasets show that our model outperforms other state-of-the-art methods.

Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its metapath based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.

Homophily, the tendency of similar nodes to connect, is a fundamental phenomenon in network science and a critical factor in the performance of graph neural networks (GNNs). While existing studies primarily explore homophily in homogeneous graphs, where nodes share the same type, real-world networks are often more accurately modeled as heterogeneous graphs (HGs) with diverse node types and intricate cross-type interactions. This structural diversity complicates the analysis of homophily, as traditional homophily metrics fail to account for distinct label spaces across node types. To address this limitation, we introduce the Cross-Type Homophily Ratio (CHR), a novel metric that quantifies homophily based on the similarity of target information across different node types. Additionally, we propose Cross-Type Homophily-guided Graph Editing (CTHGE), a novel method for improving heterogeneous graph neural networks (HGNNs) performance by optimizing cross-type connectivity using Cross-Type Homophily Ratio. Extensive experiments on five HG datasets with nine HGNNs validate the effectiveness of CTHGE, which delivers a maximum relative performance improvement of over 25% for HGNNs on node classification tasks, offering a fresh perspective on cross-type homophily in HGs learning.

Heterogeneous Graph Neural Networks (GNNs) have shown good performance as a robust deep learning-based graph representation technique and have gained much research interest. Although it has adequately taken into account networks with a number of links and nodes, heterogeneity and the volume of semantic data provide significant obstacles. The attention mechanism, having great potential in a variety of areas, is one of the most interesting new developments in deep learning. This research demonstrates a system with two crucial attributes for embedding users and movies. The proposed framework achieves multi-level semantic attention using GNNs. We incorporated IMDB and Netflix Movie and TV Show datasets and merged them into a single consolidated dataset that was further utilized for results analysis. This paper mainly contributes a technique for movie recommendation using heterogeneous graphs and multi-level Semitics. We have proposed a framework that incorporates viewer and Director as an entity. During the research, we also combined two datasets in accordance with the proposed framework. After that, we evaluated the performance of the graph neural network on the heterogeneous graph. We discovered that the proposed model outperformed the current methodologies while using the proposed technique. Our model multilevel-Semitics-based framework shows effective results.

Railway network delay evolution: A heterogeneous graph neural network approach

- Heterogeneous graph learning has emerged as a powerful paradigm for modeling complex relational data involving multiple types of nodes and edges. Unlike homogeneous graphs, heterogeneous graphs capture rich semantic relationships across diverse entities, enabling more expressive representations. This paper presents a comprehensive and structured review of heterogeneous graph learning, covering foundational theories, classical approaches, heterogeneous graph neural networks (HGNNs), and transformer-based architectures. Recent advances from 2020 to 2025, including multimodal, dynamic, and contrastive learning frameworks, are also discussed. Furthermore, we analyze experimental benchmarks, applications across domains, and key challenges such as scalability and interpretability. Finally, future research directions are outlined to guide advancements in this rapidly evolving field. Key Words: heterogeneous graphs, graph neural networks, HGNN, graph transformers, representation learning, knowledge graphs, deep learning

Heterogeneous graphs (HGs), consisting of multiple types of nodes and links, can characterize a variety of real-world complex systems. Recently, heterogeneous graph neural networks (HGNNs), as a powerful graph embedding method to aggregate heterogeneous structure and attribute information, has earned a lot of attention. Despite the ability of HGNNs in capturing rich semantics which reveal different aspects of nodes, they still stay at a coarse-grained level which simply exploits structural characteristics. In fact, rich unstructured text content of nodes also carries latent but more fine-grained semantics arising from multi-facet topic-aware factors, which fundamentally manifest why nodes of different types would connect and form a specific heterogeneous structure. However, little effort has been devoted to factorizing them. In this paper, we propose a Topic-aware Heterogeneous Graph Neural Network, named THGNN, to hierarchically mine topic-aware semantics for learning multi-facet node representations for link prediction in HGs. Specifically, our model mainly applies an alternating two-step aggregation mechanism including intra-metapath decomposition and inter-metapath mergence, which can distinctively aggregate rich heterogeneous information according to the inferential topic-aware factors and preserve hierarchical semantics. Furthermore, a topic prior guidance module is also designed to keep the quality of multi-facet topic-aware embeddings relying on the global knowledge from unstructured text content in HGs. It helps to simultaneously improve both performance and interpretability. Experimental results on three real-world HGs demonstrate that our proposed model can effectively outperform the state-of-the-art methods in the link prediction task, and show the potential interpretability of learnt multi-facet topic-aware representations.

Heterogeneous Graph Neural Networks (HGNNs) have demonstrated remarkable capabilities in capturing effective information in heterogeneous graphs, achieving outstanding performance in various learning tasks. However, the heavy dependency of HGNNs on neighbors information may result in high latency, which restricts their practicality in real-world applications. Recent studies have attempted to overcome such latency in Graph Neural Networks (GNNs) by distilling knowledge into student models that do not rely on graph structure. But these approaches primarily focus on replicating teachers' predictive outcomes while neglecting the structural knowledge they encoded. This limitation makes such approach less effective when graphs become complex, particularly on heterogeneous graphs. Motivated by this challenge, we propose HGKD, a novel hierarchical knowledge distillation framework that transfers both structural knowledge and predictive outcomes from HGNN teachers to a multi-layer perceptron student. Additionally, we provide two variants of HGKD that help the student learn from multiple teacher models through Pareto learning and incorporate low-cost neighbor information. We evaluate HGKD and its variants on a range of heterogeneous graph datasets. The results demonstrate that our student model achieves performance comparable to or exceeding that of HGNN teachers, despite not relying on graph structures during inference.

A relation-aware heterogeneous graph convolutional network for relationship prediction

Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) designed for homogeneous graphs, especially the attention mechanism and the multi-layer structure. These mechanisms bring excessive complexity, but seldom work studies whether they are really effective on heterogeneous graphs. In this paper, we conduct an in-depth and detailed study of these mechanisms and propose the Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN). To easily capture structural information, SeHGNN pre-computes the neighbor aggregation using a light-weight mean aggregator, which reduces complexity by removing overused neighbor attention and avoiding repeated neighbor aggregation in every training epoch. To better utilize semantic information, SeHGNN adopts the single-layer structure with long metapaths to extend the receptive field, as well as a transformer-based semantic fusion module to fuse features from different metapaths. As a result, SeHGNN exhibits the characteristics of a simple network structure, high prediction accuracy, and fast training speed. Extensive experiments on five real-world heterogeneous graphs demonstrate the superiority of SeHGNN over the state-of-the-arts on both accuracy and training speed.

Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-ligand binding affinity (PLA) prediction have different inductive biases, leading to different levels of generalization capability and interpretability. Intuitively, the inductive bias of an ML-based model for PLA prediction should fit in with biological mechanisms relevant for binding to achieve good predictions with meaningful reasons. To this end, we propose an interaction-based inductive bias to restrict neural networks to functions relevant for binding with two assumptions: 1) A protein-ligand complex can be naturally expressed as a heterogeneous graph with covalent and non-covalent interactions; 2) The predicted PLA is the sum of pairwise atom-atom affinities determined by non-covalent interactions. The interaction-based inductive bias is embodied by an explainable heterogeneous interaction graph neural network (EHIGN) for explicitly modeling pairwise atom-atom interactions to predict PLA from 3D structures. Extensive experiments demonstrate that EHIGN achieves better generalization capability than other state-of-the-art ML-based baselines in PLA prediction and structure-based virtual screening. More importantly, comprehensive analyses of distance-affinity, pose-affinity, and substructure-affinity relations suggest that the interaction-based inductive bias can guide the model to learn atomic interactions that are consistent with physical reality. As a case study to demonstrate practical usefulness, our method is tested for predicting the efficacy of Nirmatrelvir against SARS-CoV-2 variants. EHIGN successfully recognizes the changes in the efficacy of Nirmatrelvir for different SARS-CoV-2 variants with meaningful reasons.

Machine learning (ML) has been widely used for efficient resource allocation (RA) in wireless networks. Although superb performance is achieved on small and simple networks, most existing ML-based approaches are confronted with difficulties when heterogeneity occurs and network size expands. In this paper, specifically focusing on power control/beamforming (PC/BF) in heterogeneous device-to-device (D2D) networks, we propose a novel unsupervised learning-based framework named heterogeneous interference graph neural network (HIGNN) to handle these challenges. First, we characterize diversified link features and interference relations with heterogeneous graphs. Then, HIGNN is proposed to empower each link to obtain its individual transmission scheme after limited information exchange with neighboring links. It is noteworthy that HIGNN is scalable to wireless networks of growing sizes with robust performance after trained on small-sized networks. Numerical results show that compared with state-of-the-art benchmarks, HIGNN achieves much higher execution efficiency while providing strong performance.

Accurate prediction of Adverse Drug Reactions (ADRs) at the patient level is essential for ensuring patient safety and optimizing healthcare outcomes. Traditional machine learning-based methods primarily focus on predicting potential ADRs for drugs, but they often fall short of capturing the complexity of individual demographics and the variations in ADRs experienced by different people. In this study, a novel framework called Precise Adverse Drug Reaction (PreciseADR) for patient-level ADR prediction is proposed. The approach effectively integrates relations between patients and ADRs, and harnesses the power of heterogeneous Graph Neural Networks (GNNs) to address the limitations of traditional methods. Specifically, a heterogeneous graph representation of patients is constructed, encompassing nodes that represent patients, diseases, drugs, and ADRs. By leveraging edges in the graph, crucial connections are captured such as a patient being affected by diseases, taking specific drugs, and experiencing ADRs. Next, a GNN-based model is utilized to learn latent representations of the patient nodes and facilitate the propagation of information throughout the graph structure. By employing patient embeddings that consider their diseases and drugs, potential ADRs can be accurately predicted. The PreciseADR is dedicated to effectively capturing both local and global dependencies within the heterogeneous graph, allowing for the identification of subtle patterns and interactions that play a significant role in ADRs. To evaluate the performance of the approach, extensive experiments are conducted on a large-scale real-world healthcare dataset with adverse reports from the FDA Adverse Event Reporting System (FAERS). Experimental results demonstrate that the PreciseADR achieves superior predictive performance in identifying patient-level ADRs, surpassing the strongest baseline by 3.2% in AUC score and by 4.9% in Hit@10.