2012/03/30 by Sabrina Gaito, Gaito, Sabrina, Matteo Zignani +12
Computer Science · Physics and Astronomy · Social Sciences · #Acceleration #Artificial intelligence #Burstiness #Complex Network Analysis Techniques #Complex network #Computer network #Computer science #Context (archaeology) #Distributed computing #Dynamic network analysis #Enhanced Data Rates for GSM Evolution #FOS: Computer and information sciences #FOS: Physical sciences #Focus (optics) #Human Mobility and Location-Based Analysis #Node (physics) #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Process (computing) #Resource (disambiguation) #Social and Information Networks (cs.SI) #Social media #Social network (sociolinguistics) #World Wide Web #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1203.6744
13 pages, 7 figures
openalex publication_date 2012/03/30 · arxiv created 2012/05/25 · arxiv updated 2015/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The high level of dynamics in today's online social networks (OSNs) creates new challenges for their infrastructures and providers. In particular, dynamics involving edge creation has direct implications on strategies for resource allocation, data partitioning and replication. Understanding network dynamics in the context of physical time is a critical first step towards a predictive approach towards infrastructure management in OSNs. Despite increasing efforts to study social network dynamics, current analyses mainly focus on change over time of static metrics computed on snapshots of social graphs. The limited prior work models network dynamics with respect to a logical clock. In this paper, we present results of analyzing a large timestamped dataset describing the initial growth and evolution of Renren, the leading social network in China. We analyze and model the burstiness of link creation process, using the second derivative, i.e. the acceleration of the degree. This allows us to detect bursts, and to characterize the social activity of a OSN user as one of four phases: acceleration at the beginning of an activity burst, where link creation rate is increasing; deceleration when burst is ending and link creation process is slowing; cruising, when node activity is in a steady state, and complete inactivity.