IEEE

Multigranularity Deep Graph Convolutional Neural Network Node Clustering Leveraging Spatial Information

Abstract: In the era of information explosion, clustering analysis of graph-structured data and empty graph-structured data is of great significance for extracting the intrinsic value of data. From ...
Quanta Magazine

New Proof Settles Decades-Old Bet About Connected Networks

According to mathematical legend, Peter Sarnak and Noga Alon made a bet about optimal graphs in the late 1980s. They’ve now both been proved wrong. It started with a bet. In the late 1980s, at a ...
IEEE

Augmented Graph Convolutional Network for Enhancing Label Reachability

Abstract: Graph Convolutional Networks (GCNs) have emerged as a leading approach for semi-supervised node classification. However, due to the uneven distribution of labeled nodes in graphs, only a ...
EurekAlert!

D2-GCN: a graph convolutional network with dynamic disentanglement for node classification

Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update a ...
Scientific Research Publishing

Wang, R., Tao, Z., Zhao, R., et al. (2022) Multi-Interaction Graph Convolutional Networks for Aspectual Sentiment Analysis. Journal of Electronics and Information, 44, 1111-1118.

ABSTRACT: Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph ...
Scientific Research Publishing

Yuan, L., Wang, J., Yu, L. and Zhang, X. (2024) Syntactic Graph Attention Network for Aspect-Level Sentiment Analysis. IEEE Transactions on Artificial Intelligence, 5, 140-153.

ABSTRACT: Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph ...