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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 : | Department of Media and Communication | ||||||||||||||||||||||||||||||||||||||||||||||||
| Institution : | City University of Hong Kong | ||||||||||||||||||||||||||||||||||||||||||||||||
| E-mail Address : | j.zhu@cityu.edu.hk | ||||||||||||||||||||||||||||||||||||||||||||||||
| Tel : | 3442 7186 | ||||||||||||||||||||||||||||||||||||||||||||||||
| Co - Investigator(s) : |
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| 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 : |
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| Abstract as per original application (English/Chinese): |
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| 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: |
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| 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) |
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| Recognized international conference(s) in which paper(s) related to this research project was/were delivered : |
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| 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 |