Results 91 to 100 of about 748,129 (177)

Number of involved nodal stations: a better lymph node classification for clinical stage IA lung adenocarcinoma. [PDF]

J Natl Cancer Cent, 2023
Liu M, Miao L, Zheng R, Zhao L, Liang X, Yin S, Li J, Li C, Li M, Zhang L.   +9 more
europepmc   +1 more source

NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification

IEEE Transactions on Big Data
Recently, the emerging graph Transformers have made significant advancements for node classification on graphs. In most graph Transformers, a crucial step involves transforming the input graph into token sequences as the model input, enabling Transformer to effectively learn the node representations.
Jinsong Chen, Siyu Jiang, Kun He
openaire   +2 more sources

OWNC: Open-World Node Classification on Graphs with a Dual-Embedding Interaction Framework

Mathematics
Traditional node classification is typically conducted in a closed-world setting, where all labels are known during training, enabling graph neural network methods to achieve high performance.
Yuli Chen, Chun Wang
doaj   +1 more source

Improving Node Classification through Convolutional Networks Built on Enhanced Message-Passing Graph. [PDF]

Comput Intell Neurosci, 2022
Song Y, Lu S, Qiu D.
europepmc   +1 more source

Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs

CoRR
In the broader machine learning literature, data-generation methods demonstrate promising results by generating additional informative training examples via augmenting sparse labels. Such methods are less studied in graphs due to the intricate dependencies among nodes in complex topology structures.
Hang Cui 0001, Tarek F. Abdelzaher
openaire   +2 more sources

The Role of Alternative Lymph Node Classification Systems in Gastroenteropancreatic Neuroendocrine Neoplasms (GEP-NEN): Superiority of a LODDS Scheme Over N Category in Pancreatic NEN (pNEN). [PDF]

Horm Metab Res, 2023
Krieg S, Tunk J, Vaghiri S, Prassas D, Jann H, Mohr R, Loosen SH, Roderburg C, Maasberg S, Begum N, Luedde T, Schott M, Giesel F, Knoefel WT, Krieg A, and the members of the German NET Registry.   +15 more
europepmc   +1 more source

An improved multi-view attention network inspired by coupled P system for node classification. [PDF]

PLoS One, 2022
Liu Q, Liu X.
europepmc   +1 more source

Node Classification in Networks via Simplicial Interactions

IEEE Transactions on Neural Networks and Learning Systems
In the node classification task, it is natural to presume that densely connected nodes tend to exhibit similar attributes. Given this, it is crucial to first define what constitutes a dense connection and to develop a reliable mathematical tool for assessing node cohesiveness.
Eunho Koo, Tongseok Lim
openaire   +3 more sources

Hypergraph Representation Learning with Weighted- and Clustering-Biased Random Walks

Entropy
Hypergraphs are powerful tools for modeling complex systems because they naturally encode higher-order interactions. However, most existing hypergraph representation-learning methods still struggle to capture such high-order structures, particularly in ...
Li Liang, Shi-Ming Cai, Shi-Cai Gong
doaj   +1 more source

BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework. [PDF]

BMC Bioinformatics, 2022
Zheng X, Du H, Luo X, Tong F, Song W, Zhao D.   +5 more
europepmc   +1 more source