Visiting Speaker
Kristen Altenburger
Stanford University
Ruffled Feathers: Attribute Inference on Social Networks Beyond Homophily
Oct 25, 2018
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2:00 pm
177 Huntington Ave
11th floor
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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. While homophily describes a bias in attribute preferences for similar others, it gives limited attention to variability. In this work, we observe that attribute preferences can exhibit variation beyond what can be explained by models of homophily. We call this excess variation monophily to describe the presence of individuals with extreme preferences for a particular attribute possibly unrelated to their own attribute. We observe that monophily can induce a similarity among friends-of-friends on a network without requiring any similarity among friends. In order to independently simulate homophily and monophily in synthetic networks, we contribute a new model of social network structure that we call the overdispersed stochastic block model (oSBM), an extension of the classical stochastic block model. We use this model to demonstrate how homophily-based methods for predicting attributes on social networks based on friends, “the company you keep,” are fundamentally different from monophily-based methods based on friends-of-friends, “the company you’re kept in.” To illustrate the differences between homophily and monophily-based prediction we place particular focus on predicting gender, where homophily can be weak or nonexistent in practice. These findings offer an alternative perspective on network structure and attributes in general and prediction in particular, complicating the already difficult task of protecting privacy on social networks.

This is joint work with Johan Ugander, Assistant Professor in the Management Science & Engineering Department at Stanford University (Website:  The paper associated with this talk is at

about the speaker
Kristen is a PhD candidate in Computational Social Science in the Management Science & Engineering Department at Stanford University. Her graduate research currently focuses on developing rigorous statistical tools for characterizing social structures in networks, with a particular focus on analyzing gender in the digital society. She received her Bachelors in Mathematics from Ohio University where she also ran varsity cross country and track for the Bobcats, completed a research fellowship at Stanford Law School, and received her Masters in Statistics from Harvard University. She was previously a Member of Technical Staff in the Data Science and Cyber Analytics Department at Sandia National Laboratories in Livermore, focusing on community detection methods in networks. Last summer she joined the Social Science & Algorithms team at Netflix for an internship analyzing word-of-mouth effects and other social signals. At Stanford, she is a 2018 Data Science Scholar, a member of the Society & Algorithms Lab, and is supported in part by a National Defense Science and Engineering Graduate Fellowship. Website:

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