Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Bures-Wasserstein Means of Graphs
Finding the mean of sampled data is a fundamental task in machine learning and statistics. However, in cases where the data samples are graph objects, defining a mean is an inherently difficult task. We propose a novel f…
Graph SimilarityNode ClassificationGraph Entropy Minimization for Semi-supervised Node Classification
Node classifiers are required to comprehensively reduce prediction errors, training resources, and inference latency in the industry. However, most graph neural networks (GNN) concentrate only on one or two of them. The …
ClassificationKnowledge DistillationNode ClassificationTask-Equivariant Graph Few-shot Learning
Although Graph Neural Networks (GNNs) have been successful in node classification tasks, their performance heavily relies on the availability of a sufficient number of labeled nodes per class. In real-world situations, n…
Few-Shot LearningMeta-LearningNode ClassificationNode Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
In the graph node embedding problem, embedding spaces can vary significantly for different data types, leading to the need for different GNN model types. In this paper, we model the embedding update of a node feature as …
Graph EmbeddingLink PredictionNode ClassificationSelf-attention Dual Embedding for Graphs with Heterophily
Graph Neural Networks (GNNs) have been highly successful for the node classification task. GNNs typically assume graphs are homophilic, i.e. neighboring nodes are likely to belong to the same class. However, a number of …
InformativenessNode ClassificationGraph Inductive Biases in Transformers without Message Passing
Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing …
Graph ClassificationGraph RegressionInductive BiasNode ClassificationGraph Neural Convection-Diffusion with Heterophily
Graph neural networks (GNNs) have shown promising results across various graph learning tasks, but they often assume homophily, which can result in poor performance on heterophilic graphs. The connected nodes are likely …
Graph LearningNode ClassificationConfidence-Based Feature Imputation for Graphs with Partially Known Features
This paper investigates a missing feature imputation problem for graph learning tasks. Several methods have previously addressed learning tasks on graphs with missing features. However, in cases of high rates of missing …
Graph LearningImputationLink PredictionNode ClassificationFast Online Node Labeling for Very Large Graphs
This paper studies the online node classification problem under a transductive learning setting. Current methods either invert a graph kernel matrix with $\mathcal{O}(n^3)$ runtime and $\mathcal{O}(n^2)$ space complexity…
Node ClassificationTransductive LearningTowards Label Position Bias in Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as a powerful tool for semi-supervised node classification tasks. However, recent studies have revealed various biases in GNNs stemming from both node features and graph topology…
Node ClassificationPositionExtracting Shopping Interest-Related Product Types from the Web
Recommending a diversity of product types (PTs) is important for a good shopping experience when customers are looking for products around their high-level shopping interests (SIs) such as hiking. However, the SI-PT conn…
Node ClassificationDEGREE: Decomposition Based Explanation For Graph Neural Networks
Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data. However, the black-box nature of GNNs prevents users from understanding and trusting the models, thus hampering their appl…
Graph ClassificationNode ClassificationTokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs
Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity on the number of nodes when handling large graphs. To this end, we propose a Neighborhood Aggrega…
Data AugmentationGraph Representation LearningNode ClassificationRepresentation LearningSelf-Explainable Graph Neural Networks for Link Prediction
Graph Neural Networks (GNNs) have achieved state-of-the-art performance for link prediction. However, GNNs suffer from poor interpretability, which limits their adoptions in critical scenarios that require knowing why ce…
Link PredictionNode ClassificationPredictionGraph Propagation Transformer for Graph Representation Learning
This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the a…
Graph LearningGraph Property PredictionGraph RegressionGraph Representation Learning+2Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
The prevalence of large-scale graphs poses great challenges in time and storage for training and deploying graph neural networks (GNNs). Several recent works have explored solutions for pruning the large original graph i…
Dynamic Node ClassificationNode ClassificationChainlet Orbits: Topological Address Embedding for the Bitcoin Blockchain
The rise of cryptocurrencies like Bitcoin, which enable transactions with a degree of pseudonymity, has led to a surge in various illicit activities, including ransomware payments and transactions on darknet markets. The…
Graph Neural NetworkNode ClassificationOptimality of Message-Passing Architectures for Sparse Graphs
We study the node classification problem on feature-decorated graphs in the sparse setting, i.e., when the expected degree of a node is $O(1)$ in the number of nodes, in the fixed-dimensional asymptotic regime, i.e., the…
Graph Neural NetworkNode ClassificationEdge Directionality Improves Learning on Heterophilic Graphs
Graph Neural Networks (GNNs) have become the de-facto standard tool for modeling relational data. However, while many real-world graphs are directed, the majority of today's GNN models discard this information altogether…
Graph Neural NetworkNode ClassificationNode Classification on Non-Homophilic (Heterophilic) GraphsAddressing Heterophily in Node Classification with Graph Echo State Networks
Node classification tasks on graphs are addressed via fully-trained deep message-passing models that learn a hierarchy of node representations via multiple aggregations of a node's neighbourhood. While effective on graph…
Node ClassificationNode Classification on Non-Homophilic (Heterophilic) Graphs