Abstract
Link analysis algorithms can evaluate relationships between entities through their relationships. It was formulated based on the eigenvalue problem. Google’s PageRank is a well-known approach to determining entities’ importance through relationships, such as link prediction and community detection on online social networks. Ordinarily, computing PageRank on online large-scale social network graphs with the original approach is inefficient. Thus, a distributed PageRank computation is viable for addressing such limitations. This paper formulates the problem of minimizing the maximum number of external ties. Meanwhile, an algorithm is proposed to find the min-cut partition, which balances the number of external properties, to study the impacts of balancing the number of external properties. The experiment section evaluates the proposed algorithm performances in terms of ranking accuracy on a Twitch users dataset social network. By comparing the algorithm’s accuracy, the experiment results show that the min-cut partition with a balancing number of external properties outperforms.
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This research work is funded by the College of Arts, Media, and Technology, Chiang Mai University.
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Sangamuang, S., Sinthamrongruk, T., Mahanan, W. (2023). An Empirical Study on Min-Max External Ties to Improve Decentralized Social Graph Ranking Performance. In: Barolli, L. (eds) Advances in Networked-based Information Systems. NBiS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-031-40978-3_5
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