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Multi-scale dynamics in a massive online social network

Published: 14 November 2012 Publication History

Abstract

Data confidentiality policies at major social network providers have severely limited researchers' access to large-scale datasets. The biggest impact has been on the study of network dynamics, where researchers have studied citation graphs and content-sharing networks, but few have analyzed detailed dynamics in the massive social networks that dominate the web today. In this paper, we present results of analyzing detailed dynamics in a large Chinese social network, covering a period of 2 years when the network grew from its first user to 19 million users and 199 million edges. Rather than validate a single model of network dynamics, we analyze dynamics at different granularities (per-user, per-community, and network-wide) to determine how much, if any, users are influenced by dynamics processes at different scales. We observe independent predictable processes at each level, and find that the growth of communities has moderate and sustained impact on users. In contrast, we find that significant events such as network merge events have a strong but short-lived impact on users, and they are quickly eclipsed by the continuous arrival of new users.

Supplementary Material

PDF File (52.pdf)
Summary Review Documentation for "Multi-scale Dynamics in a Massive Online Social Network", Authors: X. Zhao, A. Sala, C. Wilson, X. Wang, S. Gaito, H. Zheng, and B. Y. Zhao

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cover image ACM Conferences
IMC '12: Proceedings of the 2012 Internet Measurement Conference
November 2012
572 pages
ISBN:9781450317054
DOI:10.1145/2398776
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 14 November 2012

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Author Tags

  1. dynamic graphs
  2. online social networks

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IMC '12
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IMC '12: Internet Measurement Conference
November 14 - 16, 2012
Massachusetts, Boston, USA

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Overall Acceptance Rate 277 of 1,083 submissions, 26%

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  • (2021)Formulation of Relationship in Bibliographic Networks Using Follow Model of Micro-Blogging2021 International Conference on Control, Automation and Information Sciences (ICCAIS)10.1109/ICCAIS52680.2021.9624635(40-45)Online publication date: 14-Oct-2021
  • (2021)Derivation and characteristics of closed‐form solutions of the fundamental equations for online user dynamicsConcurrency and Computation: Practice and Experience10.1002/cpe.661935:14Online publication date: 19-Oct-2021
  • (2020)Understanding the User Behavior of Foursquare: A Data-Driven Study on a Global ScaleIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.29922947:4(1019-1032)Online publication date: Aug-2020
  • (2020)Closed-Form Solutions of the Fundamental Equation That Describes User Dynamics in Online Social Networks2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW)10.1109/CANDARW51189.2020.00048(204-210)Online publication date: Nov-2020
  • (2020)On the Fundamental Equation of User Dynamics and the Structure of Online Social NetworksProceedings of NetSci-X 2020: Sixth International Winter School and Conference on Network Science10.1007/978-3-030-38965-9_11(155-170)Online publication date: 20-Jan-2020
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