Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Gradient Inversion Attack on Graph Neural Networks
Graph federated learning is of essential importance for training over large graph datasets while protecting data privacy, where each client stores a subset of local graph data, while the server collects the local gradien…
Federated LearningGraph ClassificationNode ClassificationScale Invariance of Graph Neural Networks
We address two fundamental challenges in Graph Neural Networks (GNNs): (1) the lack of theoretical support for invariance learning, a critical property in image processing, and (2) the absence of a unified model capable …
Graph LearningNode ClassificationNode Classification on Non-Homophilic (Heterophilic) GraphsHeterogeneous Relationships of Subjects and Shapelets for Semi-supervised Multivariate Series Classification
Multivariate time series (MTS) classification is widely applied in fields such as industry, healthcare, and finance, aiming to extract key features from complex time series data for accurate decision-making and predictio…
Activity RecognitionClassificationDynamic Time WarpingGraph Attention+3Perturbation Ontology based Graph Attention Networks
In recent years, graph representation learning has undergone a paradigm shift, driven by the emergence and proliferation of graph neural networks (GNNs) and their heterogeneous counterparts. Heterogeneous GNNs have shown…
Graph AttentionGraph Representation LearningLink PredictionNode Classification+2GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers
Graph Transformers (GTs) have demonstrated remarkable performance in incorporating various graph structure information, e.g., long-range structural dependency, into graph representation learning. However, self-attention …
Graph ClassificationGraph Representation LearningNode ClassificationRepresentation LearningCorrelation-Aware Graph Convolutional Networks for Multi-Label Node Classification
Multi-label node classification is an important yet under-explored domain in graph mining as many real-world nodes belong to multiple categories rather than just a single one. Although a few efforts have been made by uti…
ClassificationGraph MiningNode ClassificationHeterophilic Graph Neural Networks Optimization with Causal Message-passing
In this work, we discover that causal inference provides a promising approach to capture heterophilic message-passing in Graph Neural Network (GNN). By leveraging cause-effect analysis, we can discern heterophilic edges …
Causal InferenceGraph LearningGraph Neural NetworkLink Prediction+1GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification
Graph neural networks (GNNs) have emerged as powerful models for learning representations of graph data showing state of the art results in various tasks. Nevertheless, the superiority of these methods is usually support…
Node ClassificationScalable Deep Metric Learning on Attributed Graphs
We consider the problem of constructing embeddings of large attributed graphs and supporting multiple downstream learning tasks. We develop a graph embedding method, which is based on extending deep metric and unbiased c…
Contrastive LearningGraph EmbeddingLink PredictionMetric Learning+2Contrast Similarity-Aware Dual-Pathway Mamba for Multivariate Time Series Node Classification
Multivariate time series (MTS) data is generated through multiple sensors across various domains such as engineering application, health monitoring, and the internet of things, characterized by its temporal changes and h…
Dynamic Time WarpingMambaNode ClassificationTime SeriesGraph as a feature: improving node classification with non-neural graph-aware logistic regression
Graph Neural Networks (GNNs) and their message passing framework that leverages both structural and feature information, have become a standard method for solving graph-based machine learning problems. However, these app…
Node ClassificationTowards Federated Graph Learning in One-shot Communication
Federated Graph Learning (FGL) has emerged as a promising paradigm for breaking data silos among distributed private graphs. In practical scenarios involving heterogeneous distributed graph data, personalized Federated G…
Federated LearningGraph LearningNode ClassificationHigher Order Graph Attention Probabilistic Walk Networks
Graphs inherently capture dependencies between nodes or variables through their topological structure, with paths between any two nodes indicating a sequential dependency on the nodes traversed. Message Passing Neural Ne…
DiversityGraph AttentionNode ClassificationIMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs
This paper addresses domain adaptation challenges in graph data resulting from chronological splits. In a transductive graph learning setting, where each node is associated with a timestamp, we focus on the task of Semi-…
Domain AdaptationGraph LearningNode ClassificationStochastic Block ModelFrom Primes to Paths: Enabling Fast Multi-Relational Graph Analysis
Multi-relational networks capture intricate relationships in data and have diverse applications across fields such as biomedical, financial, and social sciences. As networks derived from increasingly large datasets becom…
Graph RegressionHeterogeneous Node ClassificationNode ClassificationRelation PredictionTraining a Label-Noise-Resistant GNN with Reduced Complexity
Graph Neural Networks (GNNs) have been widely employed for semi-supervised node classification tasks on graphs. However, the performance of GNNs is significantly affected by label noise, that is, a small amount of incorr…
Graph Neural NetworkNode ClassificationPartial Label LearningScaleNet: 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+1An 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+6HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning
The rapid expansion of Internet of Things (IoT) has resulted in vast, heterogeneous graphs that capture complex interactions among devices, sensors, and systems. Efficient analysis of these graphs is critical for derivin…
Computational EfficiencyGraph Representation LearningLink PredictionNode Classification+1Shedding 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+1