Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Understanding Multistationarity of Fully Open Reaction Networks
This work addresses multistationarity of fully open reaction networks equipped with mass action kinetics. We improve upon the existing results relating existence of positive feedback loops in a reaction network and multi…
EpidemiologyGraph AttentionGraph LearningFederated Graph Semantic and Structural Learning
Federated graph learning collaboratively learns a global graph neural network with distributed graphs, where the non-independent and identically distributed property is one of the major challenges. Most relative arts foc…
Graph LearningGraph Neural NetworkAmplify Graph Learning for Recommendation via Sparsity Completion
Graph learning models have been widely deployed in collaborative filtering (CF) based recommendation systems. Due to the issue of data sparsity, the graph structure of the original input lacks potential positive preferen…
Collaborative FilteringGraph LearningGraph Neural NetworkMatrix Completion+2KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
In recent years, Graph Neural Networks (GNNs) have become the de facto tool for learning node and graph representations. Most GNNs typically consist of a sequence of neighborhood aggregation (a.k.a., message-passing) lay…
Graph ClassificationGraph LearningGraph RegressionKolmogorov-Arnold Networks+2Fast Optimizer Benchmark
In this paper, we present the Fast Optimizer Benchmark (FOB), a tool designed for evaluating deep learning optimizers during their development. The benchmark supports tasks from multiple domains such as computer vision, …
AutoMLGraph LearningHyperparameter OptimizationLight-weight End-to-End Graph Interest Network for CTR Prediction in E-commerce Search
Click-through-rate (CTR) prediction has an essential impact on improving user experience and revenue in e-commerce search. With the development of deep learning, graph-based methods are well exploited to utilize graph st…
Click-Through Rate PredictionGraph EmbeddingGraph LearningGraph Sampling+1Distance Recomputator and Topology Reconstructor for Graph Neural Networks
This paper introduces novel methodologies, the Distance Recomputator and Topology Reconstructor, aimed at enhancing Graph Neural Networks (GNNs). The Distance Recomputator dynamically recalibrates node distances within k…
Graph LearningGraphSnapShot: Caching Local Structure for Fast Graph Learning
In our recent research, we have developed a framework called GraphSnapShot, which has been proven an useful tool for graph learning acceleration. GraphSnapShot is a framework for fast cache, storage, retrieval and comput…
Graph LearningRecommendation SystemsRetrievalMosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning
Graph machine learning has made significant strides in recent years, yet the integration of visual information with graph structure and its potential for improving performance in downstream tasks remains an underexplored…
Graph LearningNext Level Message-Passing with Hierarchical Support Graphs
Message-Passing Neural Networks (MPNNs) are extensively employed in graph learning tasks but suffer from limitations such as the restricted scope of information exchange, by being confined to neighboring nodes during eac…
Graph ClassificationGraph LearningGraph Property PredictionGraph Regression+1Graph Edge Representation via Tensor Product Graph Convolutional Representation
Graph Convolutional Networks (GCNs) have been widely studied. The core of GCNs is the definition of convolution operators on graphs. However, existing Graph Convolution (GC) operators are mainly defined on adjacency matr…
Graph LearningSketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
Graph Neural Networks (GNNs) are widely applied to graph learning problems such as node classification. When scaling up the underlying graphs of GNNs to a larger size, we are forced to either train on the complete graph …
Graph LearningNode ClassificationMM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction
Graph deep learning (GDL) has demonstrated impressive performance in predicting population-based brain disorders (BDs) through the integration of both imaging and non-imaging data. However, the effectiveness of GDL based…
Graph LearningRepresentation LearningDTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs
Dynamic text-attributed graphs (DyTAGs) are prevalent in various real-world scenarios, where each node and edge are associated with text descriptions, and both the graph structure and text descriptions evolve over time. …
Dynamic graph embeddingEdge ClassificationGraph LearningLink Prediction+2GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models
This work studies self-supervised graph learning for text-attributed graphs (TAGs) where nodes are represented by textual attributes. Unlike traditional graph contrastive methods that perturb the numerical feature space …
Contrastive LearningGraph LearningPrompt EngineeringA Scalable and Effective Alternative to Graph Transformers
Graph Neural Networks (GNNs) have shown impressive performance in graph representation learning, but they face challenges in capturing long-range dependencies due to their limited expressive power. To address this, Graph…
Graph LearningGraph Representation LearningRepresentation LearningA Comprehensive Survey of Foundation Models in Medicine
Foundation models (FMs) are large-scale deep learning models trained on massive datasets, often using self-supervised learning techniques. These models serve as a versatile base for a wide range of downstream tasks, incl…
Graph LearningMedical Image AnalysisSelf-Supervised LearningSurveyHiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning
Federated Graph Learning (FGL) has emerged as a promising way to learn high-quality representations from distributed graph data with privacy preservation. Despite considerable efforts have been made for FGL under either …
Graph LearningDCILP: A Distributed Approach for Large-Scale Causal Structure Learning
Causal learning tackles the computationally demanding task of estimating causal graphs. This paper introduces a new divide-and-conquer approach for causal graph learning, called DCILP. In the divide phase, the Markov bla…
Causal DiscoveryCombinatorial OptimizationGraph LearningIGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning
Deep graph learning has gained grand popularity over the past years due to its versatility and success in representing graph data across a wide range of domains. However, the pervasive issue of imbalanced graph data dist…
Graph Learning