Abstract
In this paper, we propose an approach, Role and Topic aware Independent Cascade (RTIC), to uncover information diffusion in social networks, which extracts the opinion leaders and structural hole spanners and analyze the users’ interests on specific topics. Results conducted on three real datasets show that our approach achieves substantial improvement with only limited features compared with previous methods.
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Notes
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- 2.
We define that users who have followed by at least 1000000 are the famous users, others are the normal users.
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In the following pages, we call these three data sets as “Mo Yan data set”, “Liu Xiang data set” and “Han Han data set.”.
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Acknowledgment
This research is supported by NSFC with Grant No. 61532001 and No. 61370054, and MOE-RCOE with Grant No. 2016ZD201.
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Ren, X., Zhang, Y. (2016). Predicting Information Diffusion in Social Networks with Users’ Social Roles and Topic Interests. In: Ma, S., et al. Information Retrieval Technology. AIRS 2016. Lecture Notes in Computer Science(), vol 9994. Springer, Cham. https://doi.org/10.1007/978-3-319-48051-0_30
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DOI: https://doi.org/10.1007/978-3-319-48051-0_30
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