A relation context oriented approach to identify strong ties in social networks |
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Authors: | Li Ding Dana Steil Brandon Dixon Allen Parrish David Brown |
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Affiliation: | 1. Department of Otolaryngology–Head & Neck Surgery, University of Medicine and Dentistry of New Jersey–New Jersey Medical School, Newark, NJ, USA;2. Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, NY, USA;3. Rhinology Section, North Shore University Hospital, Manhasset, NY, USA;4. Department of Otolaryngology, New York University School of Medicine, New York, NY, USA;5. Center for Skull Base and Pituitary Surgery, Neurological Institute of New Jersey, University of Medicine and Dentistry of New Jersey–New Jersey Medical School, Newark, NJ, USA;6. Department of Neurological Surgery, University of Medicine and Dentistry of New Jersey–New Jersey Medical School, Newark, NJ, USA |
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Abstract: | Strong ties play a crucial role in transmitting sensitive information in social networks, especially in the criminal justice domain. However, large social networks containing many entities and relations may also contain a large amount of noisy data. Thus, identifying strong ties accurately and efficiently within such a network poses a major challenge. This paper presents a novel approach to address the noise problem. We transform the original social network graph into a relation context-oriented edge-dual graph by adding new nodes to the original graph based on abstracting the relation contexts from the original edges (relations). Then we compute the local k-connectivity between two given nodes. This produces a measure of the robustness of the relations. To evaluate the correctness and the efficiency of this measure, we conducted an implementation of a system which integrated a total of 450 GB of data from several different data sources. The discovered social network contains 4,906,460 nodes (individuals) and 211,403,212 edges. Our experiments are based on 700 co-offenders involved in robbery crimes. The experimental results show that most strong ties are formed with k ? 2. |
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