This paper addresses the challenge of uncovering hidden affinity relationships within online communities, where discussions bring people together to share information and engage on various topics. As individuals mention themselves frequently and exchange positive messages, they gradually develop affinity towards each other. We introduce HAR-search, an algorithm based on Markov Chain Models, for discovering these hidden affinity relationships. By deriving affinity scores amongst individuals, our method allows for tracking the evolution of affinity over time and predicting relationships influenced by certain community members. Comparative analysis with state-of-the-art methods demonstrates the robustness and fine-grained detail consideration of our approach.
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Tshimula, Jean Marie, Belkacem Chikhaoui, and Shengrui Wang. "Har-search: A method to discover hidden affinity relationships in online communities." Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2019.
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