Results 51 to 60 of about 748,129 (177)
Frontiers in Artificial IntelligenceIntroductionGraph-based representations are becoming more common in the medical domain, where each node defines a patient, and the edges signify associations between patients, relating individuals with disease and symptoms in a node classification task ...
Sule Tekkesinoglu +2 more
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Unifying Structural Proximity and Equivalence for Network Embedding
IEEE Access, 2019 The fundamental purpose of network embedding is to automatically encode each node in a network as a low-dimensional vector, while at the same time preserving certain characteristics of the network.
Benyun Shi +4 more
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ALPINE : Active Link Prediction using Network Embedding [PDF]
, 2020 Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, consumer-product recommendations, and the identification of hidden interactions between actors in a crime ...
Chen, Xi +3 more
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Phishing Node Detection in Ethereum Transaction Network Using Graph Convolutional Networks
Applied Sciences, 2023 As the use of digital currencies, such as cryptocurrencies, increases in popularity, phishing scams and other cybercriminal activities on blockchain platforms (e.g., Ethereum) have also risen.
Zhen Zhang +5 more
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A Robust Advantaged Node Placement Strategy for Sparse Network Graphs
, 2018 Establishing robust connectivity in heterogeneous networks (HetNets) is an important yet challenging problem. For a HetNet accommodating a large number of nodes, establishing perturbation-invulnerable connectivity is of utmost importance.
Ding, Kai +2 more
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Pure node selection for imbalanced graph node classification
Neural NetworksThe problem of class imbalance refers to an uneven distribution of quantity among classes in a dataset, where some classes are significantly underrepresented compared to others. Class imbalance is also prevalent in graph-structured data. Graph neural networks (GNNs) are typically based on the assumption of class balance, often overlooking the issue of ...
Fanlong Zeng +3 more
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Enhancement Economic System Based-Graph Neural Network in Stock Classification
IEEE Access, 2023 As a result of the integration of the stock industry into the entire international economic system, stock companies publish hundreds of prospectuses every second.
Yaoqun Xu, Yuhang Zhang
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Node Classification in Random Trees
We propose a method for the classification of objects that are structured as random trees. Our aim is to model a distribution over the node label assignments in settings where the tree data structure is associated with node attributes (typically high dimensional embeddings).
Wouter W. L. Nuijten, Vlado Menkovski
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InformaticsThe combination of Bidirecional Encoder Representations from Transformers (BERT) and Graph Neural Networks (GNNs) has been extensively explored in the text classification literature, usually employing BERT as a feature extractor combined with ...
Eliton Luiz Scardin Perin +3 more
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Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models [PDF]
, 2015 Deep learning has shown state-of-art classification performance on datasets such as ImageNet, which contain a single object in each image. However, multi-object classification is far more challenging.
Anandkumar, Anima, Nimmagadda, Tejaswi
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