Journal
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
Volume 8, Issue 1, Pages 73-100Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2556612
Keywords
Community detection; social circles; ego networks; machine learning
Funding
- NSF [IIS-1016909, CNS-1010921, CAREER IIS-1149837, IIS-1159679]
- ARO MURI
- DARPA XDATA
- DARPA GRAPHS
- ARL AHPCRC
- Okawa Foundation
- Docomo
- Boeing
- Volkswagen
- Intel
- Alfred P. Sloan Fellowship
- Microsoft Faculty Fellowship
- Allyes
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1149837] Funding Source: National Science Foundation
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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.
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