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arXiv 1705.04774cs.SI
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社会性别结构中的偏见和差异

Bias and variance in the social structure of gender

Kristen M. Altenburger, Johan Ugander

摘要:

个体倾向于与与自己相似的人成为朋友,通常被称为同质性,这是社交网络的一个突出且研究充分的特征。许多机器学习方法利用同质性来基于个体的朋友的属性来预测个体的属性。与此同时,最近的研究表明,性别同质性在实践中可能很弱或不存在,这使得性别预测特别具有挑战性。在这项工作中,我们确定了另一个有用的结构特征,用于预测性别,性别偏好的过度分散,由对特定性别有极端偏好的个体引入,无论他们自己的性别如何。我们称这种属性为“一个人的爱”,并共同描述了在社交网络的偏好偏差和偏好方差的同质性和同质性的统计结构。对于预测,我们发现这种极端性别偏好的模式引入了朋友的朋友相关性,其中个人与他们的朋友的朋友相似,而不一定与他们的朋友相似。我们分析了美国大学的在线友谊网络和美国高中的离线友谊网络,并观察到基于朋友的预测方法与基于朋友的朋友的预测方法之间的根本差异,“你保持的公司”,“你保持的公司”。“这些发现为一般属性预测,特别是性别预测提供了另一种视角,使保护属性隐私的任务变得更加复杂。

英文摘要:

The observation that individuals tend to be friends with people who are similar to themselves, commonly known as homophily, is a prominent and well-studied feature of social networks. Many machine learning methods exploit homophily to predict attributes of individuals based on the attributes of their friends. Meanwhile, recent work has shown that gender homophily can be weak or nonexistent in practice, making gender prediction particularly challenging. In this work, we identify another useful structural feature for predicting gender, an overdispersion of gender preferences introduced by individuals who have extreme preferences for a particular gender, regardless of their own gender. We call this property monophily for "love of one," and jointly characterize the statistical structure of homophily and monophily in social networks in terms of preference bias and preference variance. For prediction, we find that this pattern of extreme gender preferences introduces friend-of-friend correlations, where individuals are similar to their friends-of-friends without necessarily being similar to their friends. We analyze a population of online friendship networks in U.S. colleges and offline friendship networks in U.S. high schools and observe a fundamental difference between the success of prediction methods based on friends, "the company you keep," compared to methods based on friends-of-friends, "the company you're kept in." These findings offer an alternative perspective on attribute prediction in general and gender in particular, complicating the already difficult task of protecting attribute privacy.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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