Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization
Graph out-of-distribution (OOD) generalization remains a major challenge in graph learning since graph neural networks (GNNs) often suffer from severe performance degradation under distribution shifts. Invariant learning…
Contrastive LearningData AugmentationGraph ClassificationGraph Learning+1You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learning
Recent research on the robustness of Graph Neural Networks (GNNs) under noises or attacks has attracted great attention due to its importance in real-world applications. Most previous methods explore a single noise sourc…
DenoisingGraph LearningNon-convolutional Graph Neural Networks
Rethink convolution-based graph neural networks (GNN) -- they characteristically suffer from limited expressiveness, over-smoothing, and over-squashing, and require specialized sparse kernels for efficient computation. H…
Graph LearningRevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation
Acquiring reviewers for academic submissions is a challenging recommendation scenario. Recent graph learning-driven models have made remarkable progress in the field of recommendation, but their performance in the academ…
Contrastive LearningGraph LearningUnveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies
In recent years, spiking neural networks (SNNs) have attracted substantial interest due to their potential to replicate the energy-efficient and event-driven processing of biological neurons. Despite this, the applicatio…
Graph LearningGraph Neural NetworkGraph Representation LearningRepresentation LearningCo-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction
Structure encoding has proven to be the key feature to distinguishing links in a graph. However, Structure encoding in the temporal graph keeps changing as the graph evolves, repeatedly computing such features can be tim…
Dynamic Link PredictionGraph LearningLink PredictionDyGKT: Dynamic Graph Learning for Knowledge Tracing
Knowledge Tracing aims to assess student learning states by predicting their performance in answering questions. Different from the existing research which utilizes fixed-length learning sequence to obtain the student st…
Graph LearningKnowledge TracingQuestion AnsweringFTF-ER: Feature-Topology Fusion-Based Experience Replay Method for Continual Graph Learning
Continual graph learning (CGL) is an important and challenging task that aims to extend static GNNs to dynamic task flow scenarios. As one of the mainstream CGL methods, the experience replay (ER) method receives widespr…
class-incremental learningClass Incremental LearningGraph LearningIncremental LearningGene Regulatory Network Inference from Pre-trained Single-Cell Transcriptomics Transformer with Joint Graph Learning
Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data is a complex challenge that requires capturing the intricate relationships between genes and their regulatory interactions. In th…
Graph LearningLifelong Graph Learning for Graph Summarization
Summarizing web graphs is challenging due to the heterogeneity of the modeled information and its changes over time. We investigate the use of neural networks for lifelong graph summarization. Assuming we observe the web…
AttributeGraph LearningSMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction
Predicting traffic accidents is the key to sustainable city management, which requires effective address of the dynamic and complex spatiotemporal characteristics of cities. Current data-driven models often struggle with…
Contrastive LearningGraph LearningManagementMasked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation
Since Multimodal Emotion Recognition in Conversation (MERC) can be applied to public opinion monitoring, intelligent dialogue robots, and other fields, it has received extensive research attention in recent years. Unlike…
Emotion RecognitionEmotion Recognition in ConversationGraph AttentionGraph Learning+2Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency
Graph Out-of-Distribution (OOD), requiring that models trained on biased data generalize to the unseen test data, has considerable real-world applications. One of the most mainstream methods is to extract the invariant s…
Graph LearningMeta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning wit…
Contrastive LearningGraph LearningMeta-LearningNode ClassificationSubgraph Clustering and Atom Learning for Improved Image Classification
In this study, we present the Graph Sub-Graph Network (GSN), a novel hybrid image classification model merging the strengths of Convolutional Neural Networks (CNNs) for feature extraction and Graph Neural Networks (GNNs)…
ClusteringDictionary LearningGraph Learningimage-classification+1All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks
Graph Neural Networks (GNNs) have attracted immense attention in the past decade due to their numerous real-world applications built around graph-structured data. On the other hand, Large Language Models (LLMs) with exte…
AllGraph LearningGLAudio Listens to the Sound of the Graph
We propose GLAudio: Graph Learning on Audio representation of the node features and the connectivity structure. This novel architecture propagates the node features through the graph network according to the discrete wav…
Graph LearningTorchGT: A Holistic System for Large-scale Graph Transformer Training
Graph Transformer is a new architecture that surpasses GNNs in graph learning. While there emerge inspiring algorithm advancements, their practical adoption is still limited, particularly on real-world graphs involving u…
Graph LearningTemporal receptive field in dynamic graph learning: A comprehensive analysis
Dynamic link prediction is a critical task in the analysis of evolving networks, with applications ranging from recommender systems to economic exchanges. However, the concept of the temporal receptive field, which refer…
BenchmarkingDynamic Link PredictionGraph LearningLink Prediction+1Continuity Preserving Online CenterLine Graph Learning
Lane topology, which is usually modeled by a centerline graph, is essential for high-level autonomous driving. For a high-quality graph, both topology connectivity and spatial continuity of centerline segments are critic…
Autonomous DrivingGraph Learning