Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Training-Free Message Passing for Learning on Hypergraphs
Hypergraphs are crucial for modelling higher-order interactions in real-world data. Hypergraph neural networks (HNNs) effectively utilise these structures by message passing to generate informative node features for vari…
ClassificationHyperedge PredictionNode ClassificationClassifying Nodes in Graphs without GNNs
Graph neural networks (GNNs) are the dominant paradigm for classifying nodes in a graph, but they have several undesirable attributes stemming from their message passing architecture. Recently, distillation methods succe…
Node ClassificationGame-theoretic Counterfactual Explanation for Graph Neural Networks
Graph Neural Networks (GNNs) have been a powerful tool for node classification tasks in complex networks. However, their decision-making processes remain a black-box to users, making it challenging to understand the reas…
counterfactualCounterfactual ExplanationDecision MakingNode ClassificationFeature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
How would randomly shuffling feature vectors among nodes from the same class affect graph neural networks (GNNs)? The feature shuffle, intuitively, perturbs the dependence between graph topology and features (A-X depende…
Node ClassificationSimilarity-based Neighbor Selection for Graph LLMs
Text-attributed graphs (TAGs) present unique challenges for direct processing by Language Learning Models (LLMs), yet their extensive commonsense knowledge and robust reasoning capabilities offer great promise for node c…
Node ClassificationMasked Graph Autoencoder with Non-discrete Bandwidths
Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored. In this paper, we unveil that the existing discrete edge masking and binary link reconstructio…
BlockingLink PredictionNode ClassificationSelf-Supervised LearningActive Learning for Graphs with Noisy Structures
Graph Neural Networks (GNNs) have seen significant success in tasks such as node classification, largely contingent upon the availability of sufficient labeled nodes. Yet, the excessive cost of labeling large-scale graph…
Active LearningNode ClassificationTowards Neural Scaling Laws on Graphs
Deep graph models (e.g., graph neural networks and graph transformers) have become important techniques for leveraging knowledge across various types of graphs. Yet, the neural scaling laws on graphs, i.e., how the perfo…
Graph ClassificationLink PredictionNode ClassificationGITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning
Large Language Models (LLMs) are increasingly used for various tasks with graph structures. Though LLMs can process graph information in a textual format, they overlook the rich vision modality, which is an intuitive way…
Link PredictionNode ClassificationScalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling
Temporal graph representation learning (TGRL) is essential for modeling dynamic systems in real-world networks. However, traditional TGRL methods, despite their effectiveness, often face significant computational challen…
GPUGraph Representation LearningLink PredictionNode Classification+1Topology-Informed Graph Transformer
Transformers have revolutionized performance in Natural Language Processing and Vision, paving the way for their integration with Graph Neural Networks (GNNs). One key challenge in enhancing graph transformers is strengt…
Graph ClassificationGraph RegressionInductive BiasNode ClassificationL2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
For analysing real-world networks, graph representation learning is a popular tool. These methods, such as a graph autoencoder (GAE), typically rely on low-dimensional representations, also called embeddings, which are o…
DecoderGraph Representation LearningNetwork EmbeddingNode Classification+1CPT: Competence-progressive Training Strategy for Few-shot Node Classification
Graph Neural Networks (GNNs) have made significant advancements in node classification, but their success relies on sufficient labeled nodes per class in the training data. Real-world graph data often exhibits a long-tai…
Meta-LearningNode ClassificationGraph Transformers without Positional Encodings
Recently, Transformers for graph representation learning have become increasingly popular, achieving state-of-the-art performance on a wide-variety of graph datasets, either alone or in combination with message-passing g…
Graph ClassificationGraph RegressionGraph Representation LearningNode Classification+1IGCN: Integrative Graph Convolution Networks for patient level insights and biomarker discovery in multi-omics integration
Developing computational tools for integrative analysis across multiple types of omics data has been of immense importance in cancer molecular biology and precision medicine research. While recent advancements have yield…
Node ClassificationGraphViz2Vec: A Structure-aware Feature Generation Model to Improve Classification in GNNs
GNNs are widely used to solve various tasks including node classification and link prediction. Most of the GNN architectures assume the initial embedding to be random or generated from popular distributions. These initia…
Classificationimage-classificationImage ClassificationLink Prediction+1Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction
In recent years, spectral graph neural networks, characterized by polynomial filters, have garnered increasing attention and have achieved remarkable performance in tasks such as node classification. These models typical…
Node ClassificationDGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations
Graph neural networks (GNNs) demonstrate a robust capability for representation learning on graphs with complex structures, showcasing superior performance in various applications. The majority of existing GNNs employ a …
AttributeGraph EmbeddingNode ClassificationRepresentation Learning+1Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem
The vanilla Graph Convolutional Network (GCN) uses a low-pass filter to extract low-frequency signals from graph topology, which may lead to the over-smoothing problem when GCN goes deep. To this end, various methods hav…
AttributeNode ClassificationFedGT: Federated Node Classification with Scalable Graph Transformer
Graphs are widely used to model relational data. As graphs are getting larger and larger in real-world scenarios, there is a trend to store and compute subgraphs in multiple local systems. For example, recently proposed …
ClassificationFederated LearningNode ClassificationOnline Clustering