Generating online social networks based on socio-demographic attributes
| Autor: | Faraz Zaidi, Céline Rozenblat, Muhammad Qasim Pasta |
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| Rok vydání: | 2017 |
| Předmět: |
Structure (mathematical logic)
Social and Information Networks (cs.SI) FOS: Computer and information sciences Physics - Physics and Society Control and Optimization Correctness Dynamic network analysis Computer Networks and Communications Computer science Applied Mathematics FOS: Physical sciences Computer Science - Social and Information Networks Demographic profile Physics and Society (physics.soc-ph) Management Science and Operations Research Preferential attachment Triadic closure World Wide Web Computational Mathematics Evolving networks Location |
| DOI: | 10.48550/arxiv.1702.01434 |
| Popis: | 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 due to an individual's demographic attributes such as age group, gender, geographic location, or due to a 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 network formation. We use different publicly available Facebook datasets as benchmarks to demonstrate the correctness of the proposed network generation model. The proposed model is flexible and thus can generate networks with varying demographic and structural properties. Comment: arXiv admin note: substantial text overlap with arXiv:1311.3508 |
| Databáze: | OpenAIRE |
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