Recently, cybercrimes that exploit the anonymity of blockchain are increasing. They steal blockchain users’ assets, threaten the network’s reliability, and destabilize the blockchain network. Therefore, it is necessary to detect blockchai...
Next Point-of-Interest (POI) recommendation is aimed at predicting users’ future visits based on their current status and historical check-in records, providing convenience to users and potential profits to businesses. The Graph Neural Network...
Learning from heterogeneous graphs under the constraints of both data scarcity and data privacy presents a significant challenge. While graph prompt learning offers a pathway for efficient few-shot adaptation, and federated learning provides a paradi...
Anti-money laundering (AML) in cryptocurrency networks presents significant challenges due to complex transactional relationships, severe class imbalance, and limited labeled data, which severely constrain the scalability and label efficiency of exis...
Multi-agent inverse reinforcement learning (MA-IRL) infers the underlying reward functions or objectives of multiple agents by observing their behavioral data, thereby providing insights into collaboration, competition, or mixed interaction strategie...
The combinatorial therapy with multiple drugs may lead to unexpected drug-drug interactions (DDIs) and result in adverse reactions to patients. Predicting DDI events can mitigate the potential risks of combinatorial therapy and enhance drug safety. I...
Since side-effects of drugs are one of the primary reasons for their failure in clinical trials, predicting their side-effects can help reduce drug development costs. We proposed a method based on heterogeneous graph transformer and capsule networks...
Driven by the rise of intelligent manufacturing and Industry 4.0, the manufacturing industry faces significant challenges in adapting to flexible and efficient production methods. This study presents an innovative approach to solving the Flexible Job...
In multi-agent reinforcement learning, the fully centralized approach suffers from issues such as explosion of the joint state and action spaces, leading to performance degradation. On the other hand, the fully decentralized approach relies on agents...
Edge embedding is a technique for constructing low-dimensional feature vectors of edges in heterogeneous graphs, which are also called heterogeneous information networks (HINs). However, edge embedding research is still in its early stages, and few w...
Detecting fraudulent users in social networks could reduce online fraud and telecommunication fraud cases, which is essential to protect the lives and properties of internet users and maintain social harmony and stability. We study how to detect frau...
Graph Neural Networks (GNNs) face fundamental algorithmic challenges in real-world applications due to a combination of data heterogeneity, adversarial heterophily, and severe class imbalance. A critical research gap exists for a unified framework th...
Along with the growth of graph neural networks (GNNs), many researchers have adopted metapath-based GNNs to handle complex heterogeneous graph embedding. The conventional definition of a metapath only distinguishes whether there is a connection betwe...
Exploring food’s rich composition and nutritional information is crucial for understanding and improving people’s dietary preferences and health habits. However, most existing food recommendation models tend to overlook the impact of food...
How to learn the embedding vectors of nodes in unsupervised large-scale heterogeneous networks is a key problem in heterogeneous network embedding research. This paper proposes an unsupervised embedding learning model, named LHGI (Large-scale Heterog...
With the implementation of conceptual labeling on online learning resources, knowledge-concept recommendations have been introduced to pinpoint concepts that learners may wish to delve into more deeply. As the core subject of learning, learners&rsquo...
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...
Graph neural networks are graph-based deep learning technologies that have attracted significant attention from researchers because of their powerful performance. Heterogeneous graph-based graph neural networks focus on the heterogeneity of the nodes...
Heterogeneous graph neural networks (HGNNs) deliver the powerful capability to model many complex systems in real-world scenarios by embedding rich structural and semantic information of a heterogeneous graph into low-dimensional representations. How...
The intricate and dynamic nature of agricultural supply chains imposes stringent demands on optimization methodologies, necessitating multi-objective considerations, privacy safeguards, and decision transparency to address pivotal challenges in ensur...
With the strong capability of heterogeneous graphs in accurately modeling various types of nodes and their interactions, they have gradually become a research hotspot, promoting the rapid development of the field of heterogeneous graph neural network...
The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing met...
The identification of drug–target interaction (DTI) is crucial for drug discovery. However, how to reduce the graph neural network’s false positives due to its bias and negative transfer in the original bipartite graph remains to be clari...
In drug development, drug-target affinity (DTA) prediction is a key indicator for assessing the drug’s efficacy and safety. Despite significant progress in deep learning-based affinity prediction approaches in recent years, there are still limi...
In this paper, we predict money laundering in Bitcoin transactions by leveraging a deep learning framework and incorporating more characteristics of Bitcoin transactions. We produced a dataset containing 46,045 Bitcoin transaction entities and 319,31...
Accurate and timely precipitation forecasting is critical for climate risk management, agriculture, and hydrological regulation. However, this task remains challenging due to the dynamic evolution of atmospheric systems, heterogeneous environmental f...
In view of the Flexible Job-shop Scheduling Problem (FJSP) under multi-product and variable-batch production modes, this paper presents an intelligent scheduling approach based on a heterogeneity-enhanced graph neural network combined with deep reinf...
Forest fires have frequently occurred and caused great harm to people’s lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features. However, it is difficult to efficiently obtain...
To address the semantic gap and complex feature entanglement inherent in multimodal emotion recognition, we propose the Dynamic Heterogeneous Graph Temporal Network (DHGTN), an end-to-end framework designed to model dynamic cross-modal interactions e...
Background: Spatially resolved transcriptomics (SRT) enables simultaneous measurement of gene expression and spatial location, but the existing domain detection methods are limited by over-reliance on spot-to-spot proximity, rigid pre-alignment requi...
Automatic algorithm selection is a critical challenge in data-driven decision-making due to the proliferation of available algorithms and the diversity of application scenarios, with no universally optimal solution. Traditional methods, including rul...
Multimodal sentiment analysis faces a number of challenges, including modality missing, modality heterogeneity gap, incomplete datasets, etc. Previous studies usually adopt schemes like meta-learning or multi-layer structures. Nevertheless, these met...
Knowledge graph completion (KGC) necessitates comprehensive modeling of heterogeneous relations by effectively integrating both graph structural information and textual semantics. Current approaches often exhibit fragmented feature utilization or sub...
The integration of heterogeneous geospatial data, specifically low-cost unmanned aerial vehicle (UAV) imagery and mobile light detection and ranging (LiDAR) system point clouds, presents a significant challenge due to the significant radiometric and...
Personalized learning resource recommendation is an essential component of intelligent tutoring systems. To address the issue of the plethora of learning resources and enhance the learner experience in intelligent tutoring systems, learning resource...
Stance detection, the task of identifying the stance expressed in a text toward a specific target, is essential for analyzing public opinion across diverse domains. The existing approaches primarily focus on modeling the semantic relationship between...
The main reason students drop out of online courses is often that they lose interest during learning. Moreover, it is not easy for students to choose an appropriate course before actually learning it. Course recommendation is necessary to address thi...
High-quality test cases are vital for ensuring software reliability and security. However, existing symbolic execution tools generally rely on single-path search strategies, have limited feature extraction capability, and exhibit unstable model predi...
Enzyme kinetic parameters
Social bots increasingly mimic real users and collaborate in large-scale influence campaigns, distorting public perception and making their detection both critical and challenging. Traditional bot detection methods, constrained by single-source featu...
Real-world systems frequently exhibit hierarchical multipartite graph structures, yet existing graph neural network (GNN) approaches lack systematic frameworks for hyperparameter optimization in heterogeneous multi-level architectures, limiting their...
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The evolution toward 6G Computing Power Networks (CPN) aims to deeply integrate multi-tier computing resources across Cloud, Edge, and end devices. However, the significant heterogeneity of computing resources, characterized by varying hardware archi...
Cross-domain decision-making systems are suffering a huge challenge with the rapidly emerging uneven quality of user-generated data, which poses a heavy responsibility to online platforms. Current content analysis methods primarily concentrate on non...
NFT prices are shaped by heterogeneous signals including visual appearance, textual narratives, transaction trajectories, and on-chain interactions, yet existing studies often model these factors in isolation and rarely unify multimodal alignment, te...
Intra-tumor heterogeneity (ITH) is a compounding factor for cancer prognoses and treatment. Single-cell DNA sequencing (scDNA-seq) provides cellular resolution of the variations in a cell and has been widely used to study cancer progression and the r...
The task of joint dialogue act recognition (DAR) and sentiment classification (DSC) aims to predict both the act and sentiment labels of each utterance in a dialogue. Existing methods mainly focus on local or global semantic features of the dialogue...
The current fraud risk in digital finance is increasing year by year, and the mainstream solutions rely on the inherent characteristics of users, which makes it difficult to explain fraud behaviors and fraud behavior patterns are less researched. To...
Personalized recommendation for online learning courses stands as a critical research topic in educational technology, where algorithmic performance directly impacts learning efficiency and user experience. To address the limitations of existing stud...
The AC Optimal Power Flow (AC-OPF) problem remains a major computational bottleneck for real-time power system operation. Conventional solvers are accurate but time-consuming, while Graph Neural Networks (GNNs) offer faster approximations yet struggl...
Change detection using heterogeneous remote sensing images is an increasingly interesting and very challenging topic. To make the heterogeneous images comparable, some graph-based methods have been proposed, which first construct a graph for the imag...
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