Abstract
Currently, online social network has derived a series of hot research problems, such as link prediction. Many results in undirected and dynamic network have been achieved. Targeted at on-line microblogs, this paper first build user’s dynamic emotional indices and network topological structure features based on time series of user’s contents and network topological information. Then, we improve artificial immune system and deploy it to predict the existence and direction of link. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of temporal information in link prediction.
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Wang, M., Ge, J., Zhang, D., Zhang, F. (2018). An Improved Artificial Immune System Model for Link Prediction. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_1
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DOI: https://doi.org/10.1007/978-3-319-97310-4_1
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