Abstract
In this book, we have introduced the current research works on broad learning and its applications on online social networks. This book has covered 4 main parts, where the first 3 parts include 6 main research directions about broad learning based social network mining problems, including (1) network alignment, (2) link prediction, (3) community detection, (4) information diffusion, (5) viral marketing, and (6) network embedding. These problems introduced in this book are all very important for many concrete real-world social network applications and services. A number of state-of-the-art algorithms have been proposed to solve these problems, which are introduced in great detail in this book. Broad learning is a very promising research area, and several potential future development directions about broad learning will be illustrated in the following sections.
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References
S. Jin, J. Zhang, P. Yu, S. Yang, A. Li, Synergistic partitioning in multiple large scale social networks, in 2014 IEEE International Conference on Big Data (Big Data) (2014)
Q. Zhan, J. Zhang, X. Pan, P. Yu, Discover tipping users for cross network influencing, in 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI) (2016)
J. Zhang, P. Yu, Multiple anonymized social networks alignment, in 2015 IEEE International Conference on Data Mining (2015)
J. Zhang, Y. Lv, P. Yu, Enterprise social link prediction, in Conference on Information and Knowledge Management (2015)
J. Zhang, P. Yu, Y. Lv, Organizational chart inference, in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’15) (2015)
J. Zhang, X. Pan, M. Li, P. Yu, Bicycle-sharing system analysis and trip prediction, in 2016 17th IEEE International Conference on Mobile Data Management (MDM) (2016)
J. Zhang, X. Pan, M. Li, P. Yu, Bicycle-sharing systems expansion: station re-deployment through crowd planning, in Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPACIAL ’16) (2016)
J. Zhang, P. Yu, Y. Lv, Q. Zhan, Information diffusion at workplace, in Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (CIKM ’16) (2016)
J. Zhang, P. Yu, Y. Lv, Enterprise employee training via project team formation, in Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM ’17) (2017)
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Zhang, J., Yu, P.S. (2019). Frontier and Future Directions. In: Broad Learning Through Fusions. Springer, Cham. https://doi.org/10.1007/978-3-030-12528-8_12
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DOI: https://doi.org/10.1007/978-3-030-12528-8_12
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