Abstract
Network completion is more challenging than link prediction, as it aims to infer both missing links and nodes. Although various methods exist for this problem, few utilize structural information-specifically, the similarity of local connection patterns. In this study, we introduce a model called C-GIN, which captures local structural patterns in the observed portions of a network using a Graph Auto-Encoder equipped with a Graph Isomorphism Network. This model generalizes these patterns to complete the entire graph. Experimental results on both synthetic and real-world networks across diverse domains indicate that C-GIN not only requires less information but also outperforms baseline prediction models in most cases. Additionally, we propose a metric known as “Reachable Clustering Coefficient (RCC)” based on network structure. Experiments reveal that C-GIN performs better on networks with higher Reachable CC values.
R. Tao and Y. Tao—Those author contribute equally
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Zhang, Z., Tao, R., Tao, Y., Qi, M., Zhang, J. (2024). Graph Completion Through Local Pattern Generalization. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1141. Springer, Cham. https://doi.org/10.1007/978-3-031-53468-3_22
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