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首页> 外文会议>IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining >HAR-search: A Method to Discover Hidden Affinity Relationships in Online Communities
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HAR-search: A Method to Discover Hidden Affinity Relationships in Online Communities

机译：HAR-search：一种发现在线社区中隐藏的亲和关系的方法

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This paper addresses the problem of discovering hidden affinity relationships in online communities. Online discussions assemble people to talk about various types of topics and to share information. People progressively develop the affinity, and they get closer as frequently as they mention themselves in messages and they send positive messages to one another. We propose an algorithm, named HAR-search, for discovering hidden affinity relationships between individuals. Based on Markov Chain Models, we derive the affinity scores amongst individuals in an online community. We show that our method allows to track the evolution of the affinity over time and to predict affinity relationships arisen from the influence of certain community members. The comparison with the state-of-the-art method shows that our method results in robust discovery and considers minute details.
机译：本文解决了在在线社区中发现隐藏的亲缘关系的问题。在线讨论使人们可以讨论各种类型的主题并共享信息。人们逐渐发展亲和力，他们在信息中提到自己的频率越来越近，并且彼此之间传递积极的信息。我们提出了一种名为HAR-search的算法，用于发现个人之间的隐藏亲缘关系。基于马尔可夫链模型，我们得出在线社区中个人之间的亲和力得分。我们证明了我们的方法可以追踪亲和力随时间的演变，并预测由于某些社区成员的影响而产生的亲和力关系。与最新方法的比较表明，我们的方法可带来可靠的发现并考虑到细微的细节。

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