Uninformed Adversarial Community Detection
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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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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
An increasingly popular branch of graph theory is the concept of community detection. However, the field of adversarial community detection currently has very little scientific literature. Initial tests suggest that an attacker in an adversarial community detection situation may not need specific information about a network as a whole in order to effectively mask a single community. In some cases, the attacker can achieve this with even random attacks on the network. However, some community detection algorithms are far more robust than others, and so the results of these tests vary greatly as a result.