Feb 9, 2021 · In this paper, we introduce a new method, called Community Attention Network (CAT), aiming to extract community-specific features and then ...
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2020 IEEE International Conference on Data Mining (ICDM). 2374-8486/20/$31.00 ©2020 IEEE. DOI 10.1109/ICDM50108.2020.00181 ...
Attention-based Graph Neural Network for Semi-supervised Learning. Paper link. Example code: PyTorch; Tags: node classification. Ying et al. Graph Convolutional ...
Apr 28, 2021 · In this paper, we consider the label dependency of graph nodes and propose a decoupling attention mechanism to learn both hard and soft ...
This work systematically study the impact of community structure on the performance of GNNs in semi-supervised node classification on graphs, and suggests ...
Jun 18, 2020 · In this paper, we propose Adaptive aggregation with Class-Attentive Diffusion (AdaCAD), a new aggregation scheme that adaptively aggregates ...
Nov 10, 2023 · Semi-supervised node classification is a crucial challenge in relational data mining and has attracted increasing interest in research on ...
Abstract. The class imbalance problem, as an important issue in learning node representations, has drawn increasing attention from the community. Although the ...
An implement of EMNLP 2019 paper "Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification" and its extension "HGAT: Heterogeneous ...
Our evaluation results on var- ious types of graph datasets show that our optimized pMRF- based method consistently outperforms existing graph neural networks ...
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