2013/11/14 by Muhammad Qasim Pasta, Zohaib Jan, Pasta, Muhammad Qasim +6 · 1 citation
Computer Science · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #FOS: Computer and information sciences #Opinion Dynamics and Social Influence #Opportunistic and Delay-Tolerant Networks #Social Media and Politics #Social and Information Networks (cs.SI) #cs.SI
paper · pdf · doi:10.48550/arxiv.1311.3508
Second International Workshop on Complex Networks and their Applications (10 pages, 8 figures)
arxiv created 2013/11/14 · openalex publication_date 2013/11/14 · arxiv updated 2013/11/15 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
Recent years have seen tremendous growth of many online social networks such as Facebook, LinkedIn and MySpace. People connect to each other through these networks forming large social communities providing researchers rich datasets to understand, model and predict social interactions and behaviors. New contacts in these networks can be formed either due to an individual's demographic profile such as age group, gender, geographic location or due to network's structural dynamics such as triadic closure and preferential attachment, or a combination of both demographic and structural characteristics. A number of network generation models have been proposed in the last decade to explain the structure, evolution and processes taking place in different types of networks, and notably social networks. Network generation models studied in the literature primarily consider structural properties, and in some cases an individual's demographic profile in the formation of new social contacts. These models do not present a mechanism to combine both structural and demographic characteristics for the formation of new links. In this paper, we propose a new network generation algorithm which incorporates both these characteristics to model growth of a network.We use different publicly available Facebook datasets as benchmarks to demonstrate the correctness of the proposed network generation model.