Graph neural networks (GNNs) have achieved remarkable success in structured prediction, owing to the GNNs’ powerful ability in learning expressive graph representations. However, most of these works learn graph representations based on a static...
Session-based recommendation (SBRS) aims to make recommendations for users merely based on the ongoing session. Existing GNN-based methods achieve satisfactory performance by exploiting the pair-wise item transition pattern; however, they ignore the...
Passenger behavior prediction aims to track passenger travel patterns through historical boarding and alighting data, enabling the analysis of urban station passenger flow and timely risk management. This is crucial for smart city development and pub...
It is crucial for both traffic management organisations and individual commuters to be able to forecast traffic flows accurately. Graph neural networks made great strides in this field owing to their exceptional capacity to capture spatial correlatio...
Diabetic retinopathy (DR) is a common complication of diabetes, which can lead to vision loss. Early diagnosis is crucial to prevent the progression of DR. In recent years, deep learning approaches have shown promising results in the development of a...
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...
Smart factories have attracted a lot of attention from scholars for intelligent scheduling problems due to the complexity and dynamics of their production processes. The dynamic job shop scheduling problem (DJSP), as one of the intelligent scheduling...
With the proliferation of Internet of Things (IoT) devices and edge nodes, edge computing has taken on much of the real-time data processing and low-latency response tasks which were previously managed by cloud computing. However, edge computing ofte...
The quantity of wind and photovoltaic power-based distributed generators (DGs) is continually rising within the distribution network, presenting obstacles to its safe, steady, and cost-effective functioning. Active distribution network dynamic reconf...
Vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) communications are two key components of intelligent transport systems (ITSs) that can share spectrum resources through in-band overlay. V2V communication primarily supports traffic safety, wherea...
The purpose of this paper is to infer a dynamic graph as a global (collective) model of time-varying measurements at a set of network nodes. This model captures both pairwise as well as higher order interactions (i.e., more than two nodes) among the...
Identifying the catalytic regioselectivity of enzymes remains a challenge. Compared to experimental trial-and-error approaches, computational methods like molecular dynamics simulations provide valuable insights into enzyme characteristics. However,...
Spatio-temporal prediction is crucial in intelligent transportation systems (ITS) to enhance operational efficiency and safety. Although Transformer-based models have significantly advanced spatio-temporal prediction performance, recent research unde...
Instructional framework based on a knowledge graph makes up for the interdisciplinary theme design ability of teachers in a single discipline, to some extent, and provides a curriculum-oriented theme generation path for STEAM instructional design. Th...
Latent variable models (LVMs) for neural population spikes have revealed informative low-dimensional dynamics about the neural data and have become powerful tools for analyzing and interpreting neural activity. However, these approaches are unable to...
The dynamic reconfiguration of active distribution networks (ADNDR) essentially belongs to a complex high-dimensional mixed-integer nonlinear stochastic optimization problem. Traditional mathematical optimization algorithms tend to encounter issues l...
Electricity load forecasting is of great significance for the overall operation of the power system and the orderly use of electricity at a later stage. However, traditional load forecasting does not consider the change in load quantity at each time...
Video summarization aims to analyze the structure and content of videos and extract key segments to construct summarization that can accurately summarize the main content, allowing users to quickly access the core information without browsing the ful...
High-Resolution Range Profile (HRRP) radar recognition suffers from data scarcity challenges in real-world applications. We present HRRPGraphNet++, a framework combining dynamic graph neural networks with meta-learning for few-shot HRRP recognition....
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...
Dyna-Q is a reinforcement learning method widely used in AGV path planning. However, in large complex dynamic environments, due to the sparse reward function of Dyna-Q and the large searching space, this method has the problems of low search efficien...
Efficient and adaptive mission planning for Earth Observation Satellites (EOSs) remains a challenging task due to the growing complexity of user demands, task constraints, and limited satellite resources. Traditional heuristic and metaheuristic appro...
The proliferation of image sharing on social media poses significant privacy risks. Although some previous works have proposed to detect privacy attributes in image sharing, they suffer from the following shortcomings: (1) reliance only on legacy arc...
Recently, bearing fault diagnosis methods based on deep learning have achieved significant success. However, in practical engineering applications, the limited labeled data and various working conditions severely constrain the widespread application...
This paper addresses the problem of learning temporal graph representations, which capture the changing nature of complex evolving networks. Existing approaches mainly focus on adding new nodes and edges to capture dynamic graph structures. However,...
The dynamic flexible job shop scheduling problem (DFJSP) with machine faults, considering the recovery condition and variable processing time, is studied to determine the rescheduling scheme when machine faults occur in real time. The Monte Carlo Tre...
The dynamic scheduling optimization of sports facilities faces challenges posed by real-time demand fluctuations and complex interdependencies between facilities. To address the adaptability limitations of traditional centralized approaches, this stu...
Online learning environments generate vast amounts of student interaction data. While these records capture observable behaviors, they do not directly reveal students’ underlying knowledge states, which are essential for tracking learning progr...
Traffic forecasting in large-scale urban networks must operate reliably under imperfect sensing conditions, where measurements may contain noise or missing values. Most existing spatio-temporal graph neural networks focus primarily on modeling spatia...
Anomaly detection aims to identify abnormal instances that significantly deviate from normal samples. With the natural connectivity between instances in the real world, graph neural networks have become increasingly important in solving anomaly detec...
Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference...
Cooperative Multi-Agent Reinforcement Learning (MARL) relies on effective coordination among agents to maximize team performance in complex environments. However, existing coordination graph-based approaches often overlook dynamic group structures an...
The efficient and accurate prediction of urban travel demand, which is a hot topic in intelligent transportation research, is challenging due to its complicated spatial-temporal dependencies, dynamic nature, and uneven distribution. Most existing for...
The dynamic flexible job-shop problem (DFJSP) is a realistic and challenging problem that many production plants face. As the product line becomes more complex, the machines may suddenly break down or resume service, so we need a dynamic scheduling f...
This paper reviews graph-theory-based methods that were recently developed in our group for post-processing molecular dynamics trajectories. We show that the use of algorithmic graph theory not only provides a direct and fast methodology to identify...
Anomalies frequently occur during the operation of spacecraft in orbit, and studying anomaly detection methods is crucial to ensure the normal operation of spacecraft. Due to the complexity of spacecraft structures, telemetry data possess characteris...
Whole-slide images (WSIs) pose significant analytical challenges due to their large data scale and complexity. Multiple instance learning (MIL) has emerged as an effective solution for WSI classification, but existing frameworks often lack flexibilit...
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...
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...
Knowledge graphs equipped with graph network networks (GNNs) have led to a successful step forward in alleviating cold start problems in recommender systems. However, the performance highly depends on precious high-quality knowledge graphs and superv...
Accurate time series forecasting is of great importance in real-world scenarios such as health care, transportation, and finance. Because of the tendency, temporal variations, and periodicity of the time series data, there are complex and dynamic dep...
The goal of this work is to evaluate a deep learning algorithm that has been designed to predict the topological evolution of dynamic complex non-Euclidean graphs in discrete–time in which links are labeled with communicative messages. This type of g...
Community detection in graphs can be viewed as the estimation of a partition map that remains stable under admissible perturbations of graph topology and node attributes. While modern graph neural networks (GNNs) achieve strong empirical accuracy, th...
In increasingly networked environments, artificial agents are required to operate not with fixed roles but with identities that adapt, evolve, and emerge through interaction. Traditional identity modeling approaches, whether symbolic or statistical,...
As software systems grow increasingly complex and interconnected, detecting vulnerabilities in source code has become a critical and challenging task. Traditional static analysis methods often fall short in capturing deep, context-dependent vulnerabi...
Multivariate time series forecasting requires modeling complex and evolving spatio-temporal dependencies as well as frequency-domain patterns; however, the existing Transformer-based approaches often struggle to effectively capture dynamic inter-seri...
In this paper, we explore the emerging role of graph neural networks (GNNs) in optimizing routing for next-generation communication networks. Traditional routing protocols, such as OSPF or the Dijkstra algorithm, often fall short in handling the comp...
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...
Temporal knowledge graphs (TKGs) are crucial for modeling evolving real-world facts and are widely applied in event forecasting and risk analysis. However, current TKG reasoning models struggle to separate causal signals from noisy observations, alig...
9 April 2024
Background: Providing care to persons with complex problems is inherently difficult due to several factors, including the impacts of proximal determinants of health, treatment response, the natural emergence of comorbidities, and service system capac...
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