This classification ... In modern data mining tasks, graphs are a powerful representation of relationships. For example, in a social network (e.g., Facebook, LinkedIn), the interaction of users can ...

Abstract: Graph neural networks (GNNs ... in GNNs precipitated by sparse node features. Comprehensive experiments on real-world datasets and ablation studies affirm that our proposed method ...

Node classification on heterogeneous graphs is a basic and critical task that remains unaddressed until the present day. Graph Neural Network is a powerful tool and has demonstrated remarkable ...

This target feature was derived from the job title of each user. A large social network of GitHub developers which was collected from the public API in June 2019. Nodes are developers who have starred ...

State diagram of different propagation ... We use different dropout ratios (0.1–0.5) on the two graph node classification data sets of Cora and Citeseer, and conduct experiments on the neural network ...

Graph Neural Networks GNNs are advanced tools for graph classification, leveraging neighborhood aggregation to update node representations iteratively. This process captures local and global graph ...