Abstract
Anchor link prediction plays a crucial role in social network analysis as it is a fundamental task. Current studies mainly rely on feature embedding of user-generated content or network structure to create latent representations. These representations are then used to determine user correspondences by mapping users between different network representation spaces. However, most studies still face some challenges such as independent representation spaces, weak discrimination of embedded representations, etc. In this paper, we propose a Multi-cascade Adversarial Network Embedding (MANE) model to tackle these challenges. MANE learns multiple representations from different cascade networks for each user, making users distinguishable in the representation space. Furthermore, an adversarial network is integrated with a mapping function to output a high-quality collection of possible anchor links for correspondence matching. Extensive experiments on real-world social network datasets demonstrate that our method can achieve the expected performance, especially in improving the top-1 precision and recall.
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Acknowledgement
This work was partially supported by the National Natural Science Foundation of China under Grant No. 61972272, the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant No. 21KJA520008, Qinlan Project of Jiangsu Province of China, and Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Zhou, J. (2024). MANE: A Multi-cascade Adversarial Network Embedding Model for Anchor Link Prediction. In: Onizuka, M., et al. Database Systems for Advanced Applications. DASFAA 2024. Lecture Notes in Computer Science, vol 14855. Springer, Singapore. https://doi.org/10.1007/978-981-97-5572-1_11
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DOI: https://doi.org/10.1007/978-981-97-5572-1_11
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