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Project Details
Funding Scheme : General Research Fund
Project Number : 154412
Project Title(English) : Growth Trajectories and Causal Mechanisms of Evolutionary Dynamics for Social Networking Services (SNSs) 
Project Title(Chinese) : 在綫社會網(SNSs)演化模型的增長軌跡與驅動原因 
Principal Investigator(English) : Prof Zhu, Jonathan Jian-hua 
Principal Investigator(Chinese) :  
Department : Dept. of Media & Communication; School of Data Science
Institution : City University of Hong Kong
E-mail Address : j.zhu@cityu.edu.hk 
Tel : 3442 7186 
Co - Investigator(s) :
Dr Peng, Taiquan
Panel : Business Studies
Subject Area : Business Studies
Exercise Year : 2012 / 13
Fund Approved : 553,717
Project Status : Completed
Completion Date : 31-3-2016
Project Objectives :
To identify and understand the growth trajectories of SNSs at both overall network level and individual node level, with specific questions such as: What does the overall growth trend look like for the whole network and for individual ego-networks? How does the rate of changes vary over time and across individuals? When do the inflections of the evolutionary process take place?
To examine and understand the working mechanism of interpersonal homophily in the initial growth of SNSs: How do new members of SNSs initiate contacts with existing members? How do the existing members reciprocate the contacts? How much is the networking process affected by homophily among the pairs? Whether, and if so, how does the homophily-driven coupling fade over time?
To examine and understand the working mechanism of preferential attachment in the later growth of SNSs: Under what conditions do members of homogenous groups begin to reach out to strangers? How do popular centers emerge? How much does the preferential attachment-driven couple contribute to rapid growth of SNSs?
Abstract as per original application
(English/Chinese):

Realisation of objectives: Objective 1 involves characterizing the trajectories (i.e., linear or nonlinear trends) of how social networks form in the first place and grow thereafter. To achieve the objective, we have collected evolutionary data from multiple social networking sites, ranging from friendship networks to microblogging networks and scientific collaborative networks, and then tested a series of theoretical models to fit the empirical data. The approach enables to us answer thoroughly the questions asked under objective 1. Objectives 2 and 3 involve testing two hypotheses (homophily and preferential attachment) about why social networks form and grow. Since the two hypotheses are competing to each other, we have carried out a series of dynamic (i.e., temporal) network analyses based on a unified model to test the hypotheses. The approach helps us to achieve objectives 2 and 3 satisfactorily.
Summary of objectives addressed:
Objectives Addressed Percentage achieved
1.To identify and understand the growth trajectories of SNSs at both overall network level and individual node level, with specific questions such as: What does the overall growth trend look like for the whole network and for individual ego-networks? How does the rate of changes vary over time and across individuals? When do the inflections of the evolutionary process take place?Yes100%
2.To examine and understand the working mechanism of interpersonal homophily in the initial growth of SNSs: How do new members of SNSs initiate contacts with existing members? How do the existing members reciprocate the contacts? How much is the networking process affected by homophily among the pairs? Whether, and if so, how does the homophily-driven coupling fade over time?Yes100%
3.To examine and understand the working mechanism of preferential attachment in the later growth of SNSs: Under what conditions do members of homogenous groups begin to reach out to strangers? How do popular centers emerge? How much does the preferential attachment-driven couple contribute to rapid growth of SNSs?Yes100%
Research Outcome
Major findings and research outcome: For objective 1, we find that the trajectories of the formation and growth of social networks follow a logistic model (i.e., an S-shaped curve) that has been repeatedly documented in the diffusion of other information and communication technologies such as telephone, television, and the internet. What’s new from our study is that we uncover that, while the trajectories at the population level follow the general logistic model, the growth trends of ego-networks at the individual level can be better modeled with several variants of logistic model, including double-logistic model (i.e., double-S curve) and power model (a rotated-L curve). We further develop a pair of concepts (global regularity and individual variability) to integrate the process. For objectives 2 and 3, we find that both homophily (i.e., similarity) between users and preferential attachment (i.e., popularity of centrally located users attracts peripheral users) have a significant impact on the formation and growth of networking ties. Furthermore, the two mechanisms operate at different stages of the process, as we hypothesized as a "stage-dependent model" in the proposal. In particular, homophily plays a greater role for new members of the network whereas preferential attachment becomes increasingly dominant as the members expand their ego-network size. We have published 10+ articles to describe the above findings and relevant methodological challenges/solutions in SCI-/SSCI-indexed journals across social science, business studies, and computer science.
Potential for further development of the research
and the proposed course of action:
One particular challenge we have encountered throughout the project is how to sample online social networks so that relevant analyses can be done more efficiently. As it comes out that this is an extremely important but largely overlooked question. We have spent a considerably amount of time in the project experimenting different sampling methods but have not been able to reach a conclusive solution. We submitted two GRF proposals to carry out follow-up studies on network sampling, but unfortunately weren't able to convince the panel members/reviewers about the importance and values of this line of research. We will pick up this research direction in the future when there is funding from an alternative source.
Layman's Summary of
Completion Report:
Online social networks have emerged as a dominant platform for members of the modern society to interact each other. However, it has remained to know exactly how the online ties are initially established and grow thereafter and, more importantly, why the ties are formed and changing. The current study address the two lines of questions based on large-scale data from multiple online social networks such as friendship networks, microblogging networks, and scientific collaborative networks. Two major findings have emerged from the study. First, the growth of online social networks demonstrates a consistent global regularity (S-curve trajectories) at the population level with considerable variability (double S-curve, rotated L-curve, etc.) at the individual level. Second, while homophily (i.e., similarity between users) is the primary driving force for the initial formation of online social ties, preferential attachment (i.e., popularity of central users attracting peripheral users) plays an increasingly important role in the subsequent growth of networking ties. Both findings bear useful implications for a wide range of applications in social, business, and technological settings.
Research Output
Peer-reviewed journal publication(s)
arising directly from this research project :
(* denotes the corresponding author)
Year of
Publication
Author(s) Title and Journal/Book Accessible from Institution Repository
2013 Xiang, L. Y.*, Zhu, J. J. H., Chen, F., & Chen, G. R.  Controllability of weighted and directed networks with nonidentical node dynamics. Mathematical Problems in Engineering, 2013, 405034.  Yes 
2013 Peng, T. Q.*, & Wang, Z. Z.  Network closure, brokerage, and structural influence of journals: A longitudinal study of journal citation network in Internet research (2000-2010). Scientometrics, 97(3), 675-693.  Yes 
2013 Wang, C. J.*, Wang, P. P., & Zhu, J. J. H.  Discussing occupy Wall Street on Twitter: Longitudinal network analysis of equality, emotion, and stability of public discussion. Cyberpsychology, Behavior and Social Networking, 16(9), 679-685.  Yes 
2013 Xu, P. P., Wu, Y. C., Wei, E. X., Peng, T. Q., Liu, S. X., Zhu, J. J. H., & Qu, H. M.  Visual analysis of topic competition on social media. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2012-2020.  Yes 
2016 Xiao-Ke Xu,∗ and Jonathan J.H. Zhu  Flexible sampling large-scale social networks by self-adjustable random walk, Physica A, 463, 356-365  No 
Haibo Hu* and Jonathan J. H. Zhu  Social networks, mass media and public opinions. Journal of Economic Interaction and Coordination.  Yes 
2014 Guodao Sun, Yingcai Wu, Shixia Liu, Tai-Quan Peng, Jonathan J. H. Zhu, and Ronghua Liang  EvoRiver: Visual Analysis of Topic Coopetition on Social Media, IEEE Transactions on Visualization and Computer Graphics, 20(12), 1753-1762  Yes 
2014 Zhenzhen Wang and Jonathan J. H. Zhu  Homophily versus preferential attachment: Evolutionary mechanisms of scientific collaboration networks. International Journal of Modern Physics C, 25(5), 1440014.  Yes 
2015 Robert Ackland and Jonathan J. H. Zhu  Social network analysis. In P. Halfpenny & R, Procter (Eds.), Innovations in digital research methods (pp. 221-244). Sage Publications.  Yes 
Hai Liang and Jonathan J. H. Zhu  Big data, collection of (social media, harvesting). In J. Matthes, C. S. Davis, & R. F. Potter (Eds.), International Handbook of Communication Methods, Wiley & Sons.  No 
2014 Zhang, L., & *Zhu, J. J. H.  Regularity and variability: Growth patterns of online friendships. International Journal of Web Services Research, 2014, 19.  No 
Recognized international conference(s)
in which paper(s) related to this research
project was/were delivered :
Month/Year/City Title Conference Name
August/2014/Beijing Using single source data to better understand user-generated content (UGC) behavior.  2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 790-795 
October/2016/Nanchang Segmenting and Characterizing Adopters of E-Books and Paper Books Based on Amazon Book Reviews  The 6th National Conference on Social Media Processing 
Other impact
(e.g. award of patents or prizes,
collaboration with other research institutions,
technology transfer, etc.):
The project has resulted in a number of collaborations with other universities or industrial firms (e.g., Microsoft Research Asia).

  SCREEN ID: SCRRM00542