A New Approach to Emotion Recognition in Conversations Based on Graph Neural Networks Research Background Emotion recognition (ER) is an important component of human-computer interaction (HCI), aiming to identify human emotional states by analyzing multimodal data such as speech, text, and video. This technology has broad application prospects in f...
Title: Generating and Detecting Spatio-Temporal Graph-Based Adversarial False Data Injection Evasion Attacks in Smart Grids Background With the continuous development of modern smart grids, the grid, as a typical Cyber-Physical System (CPS), faces numerous security threats due to the extensive exchange of data between its components. Among these, F...
Representation Learning of Lung Cancer Based on Graph Neural Networks Background Introduction With the rapid development of digital pathology, image-based diagnostic systems are becoming increasingly important in precise pathological diagnosis. These systems rely on Multiple Instance Learning (MIL) technology for Whole Slide Images (WSIs). However,...
Multiple Prior Knowledge Graph Neural Network in Multi-Omics Data Analysis Background Introduction Precision medicine is an important field for the future of healthcare as it provides personalized treatment plans for patients, improving treatment outcomes and reducing costs. For instance, due to the complex clinical, pathological, and molecular cha...
Temporal Aggregation and Propagation Graph Neural Networks (TAP-GNN) Background Introduction A temporal graph is a graph structure with dynamic interactions between nodes over continuous time, where the topology evolves over time. Such dynamic changes enable nodes to exhibit varying preferences at different times, which is critical for capturing us...
Knowledge Enhanced Graph Neural Networks for Explainable Recommendation Introduction With the explosive growth of online information, recommendation systems play an essential role in solving the problem of information overload. Traditional recommendation systems typically rely on Collaborative Filtering (CF) methods, which generate recommendations ...
A Framework for Solving Graph Optimization Problems Based on Graph Neural Networks Background and Research Motivation In solving Constraint Satisfaction Problems (CSPs) and Combinatorial Optimization Problems (COPs), a common method is the combination of backtracking and branch heuristics. Although branch heuristics designed for specific problems a...
Geometric Enhanced Pretraining for Interatomic Potentials Introduction Molecular dynamics (MD) simulations play an important role in fields such as physics, chemistry, biology, and materials science, providing insights into atomic-level processes. The accuracy and efficiency of MD simulations depend on the choice of interatomic potential functions ...
Adapting to Open-World Events via Learning Spatio-Temporal Dynamics on Mobility Networks Research Background In modern society, the Mobility-as-a-Service (MaaS) system is seamlessly integrated by various transportation modes (such as public transport, ride-sharing, and shared bicycles). To achieve efficient MaaS operation, modeling the spatio-tempo...
With the widespread application of graph-structured data in various fields, Graph Neural Networks (GNNs) have attracted significant attention as a powerful tool for analyzing graph data. However, existing GNNs primarily rely on neighborhood node similarity information when learning node representations, overlooking the potential of node differences...