Influence Maximization in Public Private Social Networks
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Date
2018-01-01
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Department
Computer Science and Electrical Engineering
Program
Computer Science
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Distribution Rights granted to UMBC by the author.
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Abstract
The public-private model is very relevant to social networks today, such as Facebook, Twitter, Google+, etc. In these social networks, some information is public, and some information is private to each user because of privacy settings. In this model, the network is different from each user's perspective, i.e., the union of the public graph and the user's private graph. Algorithmic analysis on such networks has to be adapted to each user's perspective to ensure privacy guarantees. In this work, we propose an Influence Maximization algorithm, to find a most influential seed set of a given size in public-private model of social networks. This algorithm is extended from a sketch based influence maximization algorithm. The proposed algorithm, while upholding privacy requirements, gives better influence estimate on networks having privacy settings.