Aspect-Based Sentiment Analysis With Heterogeneous Graph Neural Network

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.

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.

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.

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.

Heterogeneous graphs are an essential structure that models real-world data through different types of nodes and relationships between them, including multimodality, which comprises different types of data such as text, image, and audio. Graph Neural Networks (GNNs) are a prominent graph representation learning method that takes advantage of the graph structure and its attributes that, when applied to the multimodal heterogeneous graph, learn a unique semantic space for the different modalities. Consequently, it allows multimodal fusion through simple operators such as sum, average, or multiplication, generating unified representations considering the supplementary and complementarity relationships between the modalities. In multimodal heterogeneous graphs, the labeling process tends to be even more costly due to the multiple modalities analyzed, in addition to the imbalance of classes inherent to some applications. In order to overcome these problems in applications that comprise a class of interest, One-Class Learning (OCL) is used. Given the lack of studies on multimodal early fusion in heterogeneous graphs for OCL tasks, we proposed a method based on unsupervised GNN for heterogeneous graphs and evaluated different early fusion operators. In this paper, we extend another work by evaluating the behavior of the main GNN convolutions in the method. We highlight that using operators such as average, addition, and subtraction were the best early fusion operators. In addition, GNN layers that do not use an attention mechanism performed better. In this way, we argue for heterogeneous graph neural networks in multimodal using early fusion simple operators instead of well-often-used concatenation and less complex convolutions.

- 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

The cross-lingual text classification task remains a long-standing challenge that aims to train a classifier on high-resource source languages and apply it to classify texts in low-resource target languages, bridging linguistic gaps while maintaining accuracy. Most existing methods achieve exceptional performance by relying on multilingual pretrained language models to transfer knowledge across languages. However, little attention has been paid to factors beyond semantic similarity, which leads to the degradation of classification performance in the target languages. This study proposes a novel framework, a heterogeneous graph neural network with multi-view contrastive learning for cross-lingual text classification, which integrates a heterogeneous graph architecture with multi-view contrastive learning for the cross-lingual text classification task. This study constructs a heterogeneous graph to capture both syntactic and semantic knowledge by connecting document and word nodes using different types of edges, including Part-of-Speech tagging, dependency, similarity, and translation edges. A Graph Attention Network is applied to aggregate information from neighboring nodes. Furthermore, this study devises a multi-view contrastive learning strategy to enhance model performance by pulling positive examples closer together and pushing negative examples further apart. Extensive experiments show that the framework outperforms the previous state-of-the-art model, achieving improvements of 2.20% in accuracy and 1.96% in F1-score on the XGLUE and Amazon Review datasets, respectively. These findings demonstrate that the proposed model makes a positive impact on the cross-lingual text classification task overall.

Path planning is crucial for the safe and efficient operation of autonomous systems like UAVs, where collision detection constitutes a major computational bottleneck. While sampling-based planning methods can explore complex spaces, they often suffer from slow convergence and suboptimal path quality. Although machine learning-based approaches have strong generalization capabilities, they rely on a large amount of training data and have poor interpretability. To address these issues, this study proposes a novel mechanism based on a Heterogeneous Graph Neural Network, aimed at solving the core computational bottleneck of collision detection in path planning. Our method leverages heterogeneous graphs as data representation to effectively capture the complex interactions between obstacles and free space in planning environments. The proposed mechanism comprises two collaborative modules: a path explorer and a path smoother. The explorer identifies a candidate path, which is subsequently refined and optimized by the smoother to produce a more efficient and higher-quality path. Experimental results in simulation environments demonstrate that by embedding spatial topological and semantic prior knowledge into the heterogeneous graph structure, our method can shorten path planning time. Thus, we proposes a new approach that leverages heterogeneous graph modeling to enhance path planning efficiency.

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 Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due to diverse architectures and applied scenarios. Besides, in the research community of HGNNs, implementing and evaluating various tasks still need much human effort. To mitigate these issues, we first propose a unified framework covering most HGNNs, consisting of three components: heterogeneous linear transformation, heterogeneous graph transformation, and heterogeneous message passing layer. Then we build a platform Space4HGNN by defining a design space for HGNNs based on the unified framework, which offers modularized components, reproducible implementations, and standardized evaluation for HGNNs. Finally, we conduct experiments to analyze the effect of different designs. With the insights found, we distill a condensed design space and verify its effectiveness.

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

Herb-target interactions (HTIs) prediction plays a key role in exploring the mechanism of Traditional Chinese Medicine (TCM). There have been many studies on the prediction of drug-target interactions. However, these methods cannot be directly applied to TCM, which has the characteristics of multiple-component, multiple-target and multiple-pathway. Although network-based methods began to be used to study the prediction of HTIs, the aggregate information from the high-order neighborhood on the heterogeneous herb-target network is underutilized. Therefore, this paper proposes a Heterogeneous Graph Neural Network with Attention Mechanism for Prediction of Herb-Target Interactions (HGNA-HTI). Specifically, based on the heterogeneous herb-target graph, HGNA-HTI uses attention mechanism to give high attention values to important nodes and edges based on different types of meta relations, and applies message passing process to incorporate information from different types of links. Moreover, HGNA-HTI aggregates the above information to extract the semantic information and high-level structure of the heterogeneous herb-target graph to obtain the final feature representation to enhance the ability for prediction of HTIs. The experiment results on two datasets, show that the HGNA-HTI model is better than state-of-the-art approaches.

The utilization of graph neural networks in heterogeneous networks has been rapidly increasing in recent years. The topological structure, fundamental to the nature of graphs, is often claimed to be jointly addressed with attribute information by most heterogeneous graph neural network (HGNN) approaches. However, this assertion lacks sufficient empirical validation. We empirically challenge these claims within existing evaluation frameworks. We propose a straightforward multi-layer perceptron (MLP) based model which relies solely on adjacency relationships for feature aggregation, without fully exploiting the graph's intricate topological structures. Unexpectedly, the model achieved nearly state-of-the-art performance on several real-world datasets. This discrepancy indicates that current evaluation protocols contain systemic biases and often fail to distinguish superficial adjacency exploitation from genuine structural pattern learning. It also points to the need for future benchmark datasets and metrics that more faithfully reflect the real requirements of topological learning. We hope this work stimulates further research into the role and significance of topological information in HGNNs.

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.

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.