Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Higher Order Structures For Graph Explanations
Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data, demonstrating remarkable performance across various tasks. Recognising their importance, there has been e…
Graph 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+1DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
Graph neural networks are recognized for their strong performance across various applications, with the backpropagation algorithm playing a central role in the development of most GNN models. However, despite its effecti…
Graph LearningWhat Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding
Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-conve…
Graph LearningNode ClassificationTemporal Graph Learning Recurrent Neural Network for Traffic Forecasting
Accurate traffic flow forecasting is a crucial research topic in transportation management. However, it is a challenging problem due to rapidly changing traffic conditions, high nonlinearity of traffic flow, and complex …
Graph LearningLLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning
Recent progress in Graph Neural Networks (GNNs) has greatly enhanced the ability to model complex molecular structures for predicting properties. Nevertheless, molecular data encompasses more than just graph structures, …
Graph LearningLanguage ModelingLanguage ModellingLarge Language ModelState Space Models on Temporal Graphs: A First-Principles Study
Over the past few years, research on deep graph learning has shifted from static graphs to temporal graphs in response to real-world complex systems that exhibit dynamic behaviors. In practice, temporal graphs are formal…
Graph LearningState Space ModelsAGALE: A Graph-Aware Continual Learning Evaluation Framework
In recent years, continual learning (CL) techniques have made significant progress in learning from streaming data while preserving knowledge across sequential tasks, particularly in the realm of euclidean data. To foste…
Continual LearningGraph LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Graph Neural Networks for Brain Graph Learning: A Survey
Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology, a novel approach has emerged that invol…
Graph LearningSurveyLearning on Large Graphs using Intersecting Communities
Message Passing Neural Networks (MPNNs) are a staple of graph machine learning. MPNNs iteratively update each node's representation in an input graph by aggregating messages from the node's neighbors, which necessitates …
Graph LearningLEMMANode ClassificationCan Graph Learning Improve Planning in LLM-based Agents?
Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests in natural language into solvable sub-tas…
Decision MakingGraph LearningNavigatePrompt Engineering+1Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs
Anomaly detection in continuous-time dynamic graphs is an emerging field yet under-explored in the context of learning algorithms. In this paper, we pioneer structured analyses of link-level anomalies and graph represent…
Anomaly DetectionGraph LearningGraph Representation LearningLink Prediction+1Cross-Context Backdoor Attacks against Graph Prompt Learning
Graph Prompt Learning (GPL) bridges significant disparities between pretraining and downstream applications to alleviate the knowledge transfer bottleneck in real-world graph learning. While GPL offers superior effective…
Backdoor AttackComputational EfficiencyGraph ClassificationGraph Learning+2FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks
Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness through the lens of spectral graph theory…
FairnessGraph LearningGraph Condensation for Open-World Graph Learning
The burgeoning volume of graph data presents significant computational challenges in training graph neural networks (GNNs), critically impeding their efficiency in various applications. To tackle this challenge, graph co…
Graph LearningSmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection
The smoothing issue in graph learning leads to indistinguishable node representations, posing significant challenges for graph-related tasks. However, our experiments reveal that this problem can uncover underlying prope…
Anomaly DetectionGraph LearningGraph Neural NetworkNode Identifiers: Compact, Discrete Representations for Efficient Graph Learning
We present a novel end-to-end framework that generates highly compact (typically 6-15 dimensions), discrete (int4 type), and interpretable node representations, termed node identifiers (node IDs), to tackle inference cha…
Computational EfficiencyGraph ClassificationGraph ClusteringGraph Learning+4Repeat-Aware Neighbor Sampling for Dynamic Graph Learning
Dynamic graph learning equips the edges with time attributes and allows multiple links between two nodes, which is a crucial technology for understanding evolving data scenarios like traffic prediction and recommendation…
Graph LearningLink PredictionRecommendation SystemsTraffic PredictionGraph Sparsification via Mixture of Graphs
Graph Neural Networks (GNNs) have demonstrated superior performance across various graph learning tasks but face significant computational challenges when applied to large-scale graphs. One effective approach to mitigate…
Graph LearningMixture-of-ExpertsLearning Geospatial Region Embedding with Heterogeneous Graph
Learning effective geospatial embeddings is crucial for a series of geospatial applications such as city analytics and earth monitoring. However, learning comprehensive region representations presents two significant cha…
Graph LearningRepresentation Learning