Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
ScaleNet: Scale Invariance Learning in Directed Graphs
Graph Neural Networks (GNNs) have advanced relational data analysis but lack invariance learning techniques common in image classification. In node classification with GNNs, it is actually the ego-graph of the center nod…
ClassificationGraph Learningimage-classificationImage Classification+1Federated Graph Learning with Graphless Clients
Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs), for multiple clients, each with its own graph data. Existing methods usually assume that each client h…
Graph LearningKnowledge DistillationTransfer LearningContinuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning
The increasing demand for robust security solutions across various industries has made Video Anomaly Detection (VAD) a critical task in applications such as intelligent surveillance, evidence investigation, and violence …
Anomaly DetectionEdge-computingGraph LearningGraph Neural Network+1Fast and Robust Contextual Node Representation Learning over Dynamic Graphs
Real-world graphs grow rapidly with edge and vertex insertions over time, motivating the problem of efficiently maintaining robust node representation over evolving graphs. Recent efficient GNNs are designed to decouple …
Graph LearningRepresentation LearningAn Efficient Memory Module for Graph Few-Shot Class-Incremental Learning
Incremental graph learning has gained significant attention for its ability to address the catastrophic forgetting problem in graph representation learning. However, traditional methods often rely on a large number of la…
class-incremental learningClass Incremental LearningFew-Shot Class-Incremental LearningGraph Learning+6Shedding Light on Problems with Hyperbolic Graph Learning
Recent papers in the graph machine learning literature have introduced a number of approaches for hyperbolic representation learning. The asserted benefits are improved performance on a variety of graph tasks, node class…
Graph LearningGraph Representation LearningLink PredictionNode Classification+1A Survey on Kolmogorov-Arnold Network
This systematic review explores the theoretical foundations, evolution, applications, and future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by the Kolmogorov-Arnold representation theo…
Computational EfficiencyGraph LearningKolmogorov-Arnold NetworksSurvey+1GFT: Graph Foundation Model with Transferable Tree Vocabulary
Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Graph Foundation Models (GFMs) with broade…
Drug DiscoveryGraph LearningDistributed-Order Fractional Graph Operating Network
We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fractional calculus. Unlike traditional continuo…
Graph LearningGraph Neural NetworkQuery-Efficient Adversarial Attack Against Vertical Federated Graph Learning
Graph neural network (GNN) has captured wide attention due to its capability of graph representation learning for graph-structured data. However, the distributed data silos limit the performance of GNN. Vertical federate…
Adversarial AttackFederated LearningGraph LearningGraph Neural Network+3Do graph neural network states contain graph properties?
Deep neural networks (DNNs) achieve state-of-the-art performance on many tasks, but this often requires increasingly larger model sizes, which in turn leads to more complex internal representations. Explainability techni…
DiagnosticGraph LearningGraph Neural NetworkAgainst Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning
Federated Graph Learning (FGL) aims to collaboratively and privately optimize graph models on divergent data for different tasks. A critical challenge in FGL is to enable effective yet efficient federated optimization ag…
Graph LearningPrompt LearningTransfer LearningEnhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks
The message-passing mechanism of graph convolutional networks (i.e., GCNs) enables label information to be propagated to a broader range of neighbors, thereby increasing the utilization of labels. However, the label info…
Contrastive LearningGraph LearningRepresentation LearningPageRank Bandits for Link Prediction
Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion. Numerous research efforts have been directed at solving this problem, including …
Decision MakingGraph LearningKnowledge Graph CompletionLink Prediction+4Online Graph Learning via Time-Vertex Adaptive Filters: From Theory to Cardiac Fibrillation
Graph Signal Processing (GSP) provides a powerful framework for analysing complex, interconnected systems by modelling data as signals on graphs. Recent advances in GSP have enabled the learning of graph structures from …
Graph LearningG-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning
Graphs play a central role in modeling complex relationships across various domains. Most graph learning methods rely heavily on neighborhood information, raising the question of how to handle cold-start nodes - nodes wi…
Graph LearningLink PredictionNode ClassificationNode ClusteringGraph Learning for Numeric Planning
Graph learning is naturally well suited for use in symbolic, object-centric planning due to its ability to exploit relational structures exhibited in planning domains and to take as input planning instances with arbitrar…
Graph LearningInterpretable Machine LearningRAGraph: A General Retrieval-Augmented Graph Learning Framework
Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In thi…
Graph ClassificationGraph LearningLink PredictionNode Classification+1Network Games Induced Prior for Graph Topology Learning
Learning the graph topology of a complex network is challenging due to limited data availability and imprecise data models. A common remedy in existing works is to incorporate priors such as sparsity or modularity which …
Graph LearningEnd-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial
Cognitive diagnosis (CD) utilizes students' existing studying records to estimate their mastery of unknown knowledge concepts, which is vital for evaluating their learning abilities. Accurate CD is extremely challenging …
cognitive diagnosisGraph Learning