Direct Versus Indirect Peer Influence in Large Social Networks
时 间：2017年1月4日（周三）下午 14:30-16:00
地 点：体育外围平台APP紫金港校区行政楼1002会议室
主讲人：Prof. ZHANG Bin, University of Arizona
主持人：陈熹教授，体育外围平台APP
摘 要：
With the availability of large scale network data, peer influence in technology diffusion can be more rigorously examined and understood. Peer influence can arise from immediate neighbors in the network (formally defined as cohesion or direct ties with one-hop neighbors) and from indirect peers who share common neighbors (formally defined as structural equivalence or indirect ties with two-hop neighbors). While the literature examined the role of each peer influence (direct or indirect) separately, the study of both peer network effects acting simultaneously was ignored, largely due to methodological constraints. This paper attempts to fill this gap by evaluating the simultaneous effect of both direct and indirect peer influences in the context of the adoption of Caller Ring Back Tone (CRBT) in a cellular telephone network using data from 200 million calls by 1.4 million users. Given this large-scale network that makes traditional social network analysis intractable, we extract many densely-connected and self-contained subpopulations from the network. We find a regularity in these subpopulations in that they consist either of about 200 nodes or about 500 nodes. Using these sub-populations and panel data, we analyze direct and indirect peer influences using a novel auto-probit model with multiple network terms (direct and indirect peer influence, with homophily as a control variable). Our identification strategy relies on Bramoullé et al.'s (2009) spatial autoregressive model, allowing us to identify the direct and indirect peer influences on each of the subpopulation extracted. We use meta-analysis to summarize the estimated parameters from all subpopulations. The results show CRBT adoption to be simultaneously determined by both direct and indirect peer influence (while controlling for homophily and centrality). Robustness checks show model fit to improve when both peer influences are included. The size and direction of the two peer influences, however, differ by group size. Interestingly, indirect peer influence (structural equivalence) has a negative effect on diffusion when the group size is about 200, but it has a positive effect when the group size is about 500. The role of direct peer influence (cohesion), on the other hand, is always positive, irrespective of group size. Our findings imply that businesses must design different target strategies for large versus small groups; for large groups, businesses should focus on consumers with both multiple one-hop and two-hop neighbors; for small groups, businesses should only focus on consumers with multiple one-hop neighbors.
主讲人简介：
ZHANG Bin is an assistant professor in the Department of Management Information Systems, University of Arizona (UA), and a visiting research fellow at Carnegie Mellon University (CMU). He is also an affiliated member of UA’s Artificial Intelligence Lab. Bin received his Ph.D. degree in Information Systems Management from CMU, and a Master's degree in Machine Learning, from CMU's School of Computer Science. His primary research interests are large social network analysis and statistical modeling for network problems. Bin's research projects have been funded by federal agencies such as NSF and NIH. His work has appeared in premier information systems journals and conferences. Bin also has experience in the Internet industry at companies like Yahoo! and has designed architectures of online ERP systems in the software industry.
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数据科学与管理工程学系
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