Abstract
Graph Neural Networks (GNNs) have made remarkable achievements on various graph tasks such as node classification, link prediction, and graph clustering. However, most GNNs focus on undirected graphs and limited effort has been made to handle directed graphs (digraphs) due to their uniqueness and complexity.
In this article, we focus on directed graphs and propose DNFS, a Digraph Neural network model with the First-order and the Second-order similarities, which can preserve unique directional information and explore complex higher-order information in the digraphs. DNFS differentiates the in-degree and out-degree similarity matrices to carefully represent directional information of the digraphs, and it considers both the first-order and the second-order similarities to capture subtle structural information. Extensive experiments on public benchmark datasets demonstrate the effectiveness of our model compared to state-of-the-art baselines.
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Liu, Y., Jia, A.L. (2023). DNFS: A Digraph Neural Network with the First-Order and the Second-Order Similarity. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14257. Springer, Cham. https://doi.org/10.1007/978-3-031-44216-2_27
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