Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification
This addresses a domain-specific challenge in AI applications by improving classification performance through better modeling of complex data relationships, though it appears incremental as it builds on existing hyper-graph methods.
The paper tackles the problem of graph-based semi-supervised node classification by proposing a density-aware hyper-graph neural network to exploit high-order correlations beyond pairwise connections, with experiments on benchmark datasets showing its effectiveness.
Graph-based semi-supervised learning, which can exploit the connectivity relationship between labeled and unlabeled data, has been shown to outperform the state-of-the-art in many artificial intelligence applications. One of the most challenging problems for graph-based semi-supervised node classification is how to use the implicit information among various data to improve the performance of classifying. Traditional studies on graph-based semi-supervised learning have focused on the pairwise connections among data. However, the data correlation in real applications could be beyond pairwise and more complicated. The density information has been demonstrated to be an important clue, but it is rarely explored in depth among existing graph-based semi-supervised node classification methods. To develop a flexible and effective model for graph-based semi-supervised node classification, we propose a novel Density-Aware Hyper-Graph Neural Networks (DA-HGNN). In our proposed approach, hyper-graph is provided to explore the high-order semantic correlation among data, and a density-aware hyper-graph attention network is presented to explore the high-order connection relationship. Extensive experiments are conducted in various benchmark datasets, and the results demonstrate the effectiveness of the proposed approach.