Papers Graph structure learning
“Graph structure learning” 태그가 달린 논문 161편 · 필터 해제
Multi-Modal Video Dialog State Tracking in the Wild
We present MST-MIXER - a novel video dialog model operating over a generic multi-modal state tracking scheme. Current models that claim to perform multi-modal state tracking fall short of two major aspects: (1) They eith…
dialog state trackingGraph structure learningLanguage ModelingLanguage ModellingGraph Structure Learning with Interpretable Bayesian Neural Networks
Graphs serve as generic tools to encode the underlying relational structure of data. Often this graph is not given, and so the task of inferring it from nodal observations becomes important. Traditional approaches formul…
Graph structure learningInductive BiasUncertainty QuantificationGraph Neural Networks in Histopathology: Emerging Trends and Future Directions
Histopathological analysis of Whole Slide Images (WSIs) has seen a surge in the utilization of deep learning methods, particularly Convolutional Neural Networks (CNNs). However, CNNs often fall short in capturing the int…
Graph structure learningwhole slide imagesMotif-driven Subgraph Structure Learning for Graph Classification
To mitigate the suboptimal nature of graph structure, Graph Structure Learning (GSL) has emerged as a promising approach to improve graph structure and boost performance in downstream tasks. Despite the proposal of numer…
ClassificationGraph ClassificationGraph structure learningSpatiotemporal Graph Neural Network Modelling Perfusion MRI
Perfusion MRI (pMRI) offers valuable insights into tumor vascularity and promises to predict tumor genotypes, thus benefiting prognosis for glioma patients, yet effective models tailored to 4D pMRI are still lacking. Thi…
Graph Neural NetworkGraph structure learningPrognosisTalos: A More Effective and Efficient Adversarial Defense for GNN Models Based on the Global Homophily of Graphs
Graph neural network (GNN) models play a pivotal role in numerous tasks involving graph-related data analysis. Despite their efficacy, similar to other deep learning models, GNNs are susceptible to adversarial attacks. E…
Adversarial DefenseGraph Neural NetworkGraph structure learningS$^2$GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis
Previous graph-based approaches in Aspect based Sentiment Analysis(ABSA) have demonstrated impressive performance by utilizing graph neural networks and attention mechanisms to learn structures of static dependency trees…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Graph LearningGraph structure learning+1Programming knowledge tracing based on heterogeneous graph representation
Existing programming knowledge tracing methods have not deeply explored the relationships between knowledge concepts and programming questions or students’ codes, which leads to insufficient representation of programming…
Graph structure learningKnowledge TracingRepresentation LearningLearning Latent Graph Structures and their Uncertainty
Within a prediction task, Graph Neural Networks (GNNs) use relational information as an inductive bias to enhance the model's accuracy. As task-relevant relations might be unknown, graph structure learning approaches hav…
Graph structure learningInductive BiasPredictionLearning Social Graph for Inactive User Recommendation
Social relations have been widely incorporated into recommender systems to alleviate data sparsity problem. However, raw social relations don't always benefit recommendation due to their inferior quality and insufficient…
Graph structure learningRecommendation SystemsGraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extraction
Information extraction (IE) is an important task in Natural Language Processing (NLP), involving the extraction of named entities and their relationships from unstructured text. In this paper, we propose a novel approach…
Graph structure learningJoint Entity and Relation ExtractionRelationRelation Extraction+1DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise
Graph Neural Networks (GNNs) have achieved notable success in various applications over graph data. However, recent research has revealed that real-world graphs often contain noise, and GNNs are susceptible to noise in t…
Graph Neural NetworkGraph structure learningSelf-Supervised LearningFrom Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations
With the development of astronomical facilities, large-scale time series data observed by these facilities is being collected. Analyzing anomalies in these astronomical observations is crucial for uncovering potential ce…
Anomaly DetectionDecoderGraph Neural NetworkGraph structure learning+3LAN: Learning Adaptive Neighbors for Real-Time Insider Threat Detection
Enterprises and organizations are faced with potential threats from insider employees that may lead to serious consequences. Previous studies on insider threat detection (ITD) mainly focus on detecting abnormal users or …
Anomaly DetectionGraph structure learningDecentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning
Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server, with each agent solving varied tasks over time. To achieve efficient co…
Computational EfficiencyGraph structure learningRobust Graph Structure Learning under Heterophily
Graph is a fundamental mathematical structure in characterizing relations between different objects and has been widely used on various learning tasks. Most methods implicitly assume a given graph to be accurate and comp…
Graph Representation LearningGraph structure learningRepresentation LearningSelf-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding
In representation learning on graph-structured data, many popular graph neural networks (GNNs) fail to capture long-range dependencies, leading to performance degradation. Furthermore, this weakness is magnified when the…
Graph LearningGraph structure learningRepresentation LearningGraphEdit: Large Language Models for Graph Structure Learning
Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures. Graph Neural Networks (GNNs) have emerged as promisin…
Graph structure learningDSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning
We investigate the replay buffer in rehearsal-based approaches for graph continual learning (GCL) methods. Existing rehearsal-based GCL methods select the most representative nodes for each class and store them in a repl…
Continual LearningDiversityGraph structure learningStructural Knowledge Informed Continual Multivariate Time Series Forecasting
Recent studies in multivariate time series (MTS) forecasting reveal that explicitly modeling the hidden dependencies among different time series can yield promising forecasting performance and reliable explanations. Howe…
Continual LearningGraph structure learningMultivariate Time Series ForecastingTime Series+1