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Discovering Social Circles in Ego Networks

机译:在自我网络中发现社交圈

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People's personal social networks are big and cluttered, and currently there is no good way to automatically organize them. Social networking sites allow users to manually categorize their friends into social circles (e.g., "circles" on Google+, and "lists" on Facebook and Twitter). However, circles are laborious to construct and must be manually updated whenever a user's network grows. In this article, we study the novel task of automatically identifying users' social circles. We pose this task as a multimembership node clustering problem on a user's ego network, a network of connections between her friends. We develop a model for detecting circles that combines network structure as well as user profile information. For each circle, we learn its members and the circle-specific user profile similarity metric. Modeling node membership to multiple circles allows us to detect overlapping as well as hierarchically nested circles. Experiments show that our model accurately identifies circles on a diverse set of data from Facebook, Google+, and Twitter, for all of which we obtain hand-labeled ground truth.
机译:人们的个人社交网络庞大而混乱,目前还没有自动组织它们的好方法。社交网站允许用户将朋友手动分类到社交圈中(例如Google+上的“圈子”,以及Facebook和Twitter上的“列表”)。但是,圈的构建很费力,每当用户网络增长时,都必须手动更新。在本文中,我们研究了自动识别用户社交圈的新任务。我们将此任务视为用户的自我网络(她的朋友之间的联系网络)上的多成员节点群集问题。我们开发了一种用于检测圈子的模型,该模型结合了网络结构以及用户个人资料信息。对于每个圈子,我们都将了解其成员以及圈子特定的用户个人资料相似性指标。将节点隶属关系建模为多个圆,使我们能够检测重叠以及分层嵌套的圆。实验表明,我们的模型可以根据来自Facebook,Google +和Twitter的各种数据准确地识别圆,对于这些数据,我们都获得了手工标记的地面事实。

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