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   Articles with "node classification" as a keyword



Graph Decipher: A transparent dual‐attention graph neural network to understand the message‐passing mechanism for the node classification

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recommendations! Published in 2022 at "International Journal of Intelligent Systems"

DOI: 10.1002/int.22966

Abstract: Graph neural networks (GNNs) can be effectively applied to solve many real‐world problems across widely diverse fields. Their success is inseparable from the message‐passing mechanisms evolving over the years. However, current mechanisms treat all node… read more here.

Keywords: node classification; message passing; graph; graph neural ... See more keywords
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Node classification over bipartite graphs through projection

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recommendations! Published in 2020 at "Machine Learning"

DOI: 10.1007/s10994-020-05898-0

Abstract: Many real-world large datasets correspond to bipartite graph data settings—think for example of users rating movies or people visiting locations. Although there has been some prior work on data analysis with such bigraphs, no general… read more here.

Keywords: bipartite graphs; classification; classification bipartite; projection ... See more keywords

Dynamic graph structure evolution for node classification with missing attributes

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recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-09840-z

Abstract: Graph neural networks (GNN) have achieved remarkable success in various domains, yet incomplete node attribute data can significantly impair their performance. Graph completion learning (GCL) methods have been developed to address this issue, aiming to… read more here.

Keywords: classification missing; structure; node classification; missing attributes ... See more keywords

Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs

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recommendations! Published in 2021 at "Journal of Statistical Mechanics: Theory and Experiment"

DOI: 10.1088/1742-5468/ac21d3

Abstract: This article unveils a new relation between the Nishimori temperature parametrizing a distribution P and the Bethe free energy on random Erdős–Rényi graphs with edge weights distributed according to P. Estimating the Nishimori temperature being… read more here.

Keywords: method node; method; nishimori temperature; spectral method ... See more keywords

Minority-Weighted Graph Neural Network for Imbalanced Node Classification in Social Networks of Internet of People

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recommendations! Published in 2023 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2022.3200964

Abstract: Social networks are an essential component of the Internet of People (IoP) and play an important role in stimulating interactive communication among people. Graph convolutional networks provide methods for social network analysis with its impressive… read more here.

Keywords: network; classification; social networks; graph neural ... See more keywords

NC_SGOI: A Node Classification Method for a Streaming Graph Using Lightweight Variable Graph Neural Network

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recommendations! Published in 2025 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2025.3593327

Abstract: With the rapid development of mobile Internet in recent years, a large scale of continuous arrival correlative data, namely dynamic streaming graph, are extensively generated in various application fields. Analyzing, mining and making good use… read more here.

Keywords: classification; classification method; node classification; sgoi ... See more keywords

A Node Classification-Based Multiobjective Evolutionary Algorithm for Community Detection in Complex Networks

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recommendations! Published in 2024 at "IEEE Transactions on Computational Social Systems"

DOI: 10.1109/tcss.2022.3223159

Abstract: Multiobjective evolutionary algorithms (MOEAs) have been widely used in community detection in recent years. However, most of the existing MOEA-based ones adopted the same search strategies for all nodes and ignored the differences between the… read more here.

Keywords: node classification; community; community detection; classification based ... See more keywords

PAMT: A Novel Propagation-Based Approach via Adaptive Similarity Mask for Node Classification

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recommendations! Published in 2024 at "IEEE Transactions on Computational Social Systems"

DOI: 10.1109/tcss.2024.3387487

Abstract: Semisupervised node classification on attributed networks is a crucial task for network analysis. By decoupling two critical operations in graph convolutional networks (GCNs), namely feature transformation and neighborhood aggregation, recent works of decoupled GCNs could… read more here.

Keywords: propagation; node classification; mask; similarity mask ... See more keywords

Imbalanced Node Classification With Synthetic Over-Sampling

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recommendations! Published in 2024 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2024.3443160

Abstract: In recent years, graph neural networks (GNNs) have achieved state-of-the-art performance for node classification. However, most existing GNNs would suffer from the graph imbalance problem. In many real-world scenarios, node classes are imbalanced, with some… read more here.

Keywords: classification; synthetic sampling; node classification; imbalanced node ... See more keywords

Self-Paced Co-Training of Graph Neural Networks for Semi-Supervised Node Classification.

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recommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"

DOI: 10.1109/tnnls.2022.3157688

Abstract: Graph neural networks (GNNs) have demonstrated great success in many graph data-based applications. The impressive behavior of GNNs typically relies on the availability of a sufficient amount of labeled data for model training. However, in… read more here.

Keywords: neural networks; training; semi supervised; self paced ... See more keywords

Neighborhood Pattern Is Crucial for Graph Convolutional Networks Performing Node Classification.

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recommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"

DOI: 10.1109/tnnls.2022.3229721

Abstract: Graph convolutional networks (GCNs) are widely believed to perform well in the graph node classification task, and homophily assumption plays a core rule in the design of previous GCNs. However, some recent advances on this… read more here.

Keywords: topology; graph convolutional; convolutional networks; graph ... See more keywords