Abstract
A signed network is widely observed and constructed from the real world and is superior for containing rich information about the signs of edges. Several embedding methods have been proposed for signed networks. Current methods mainly focus on proximity similarity and the fulfillment of social psychological theories. However, no signed network embedding method has focused on structural similarity. Therefore, in this research, we propose a novel notion of degree in signed networks and a distance function to measure the similarity between two complex degrees and a node-embedding method based on structural similarity. Experiments on five network topologies, an inverted karate club network, and three real networks demonstrate that our proposed method embeds nodes with similar structural features close together and shows the superiority of a link sign prediction task from embeddings compared with the state-of-the-art methods.
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This work was supported by JST SPRING, Grant Number JPMJSP2108.
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Liu, S., Toriumi, F., Zeng, X., Nishiguchi, M., Nakai, K. (2023). SignedS2V: Structural Embedding Method for Signed Networks. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Miccichè, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-21127-0_28
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