Abstract
Predicting the evolution of a dynamic network—the addition of new edges and the removal of existing edges—is challenging. In part, this is because: (1) networks are often noisy; (2) various performance measures emphasize different aspects of prediction; and (3) it is not clear which network features are useful for prediction. To address these challenges, we develop a novel framework for robust dynamic network prediction using an adversarial formulation that leverages both edge-based and global network features to make predictions. We conduct experiments on five distinct dynamic network datasets to show the superiority of our approach compared to state-of-the-art methods.
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Li, J., Ziebart, B., Berger-Wolf, T. (2018). A Game-Theoretic Adversarial Approach to Dynamic Network Prediction. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_53
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DOI: https://doi.org/10.1007/978-3-319-93040-4_53
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