| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | September 5, 2013 |
| Latest Amendment Date: | June 23, 2017 |
| Award Number: | 1321083 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Maria Zemankova
IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2013 |
| End Date: | September 30, 2017 (Estimated) |
| Total Intended Award Amount: | $499,992.00 |
| Total Awarded Amount to Date: | $515,992.00 |
| Funds Obligated to Date: |
FY 2014 = $16,000.00 FY 2015 = $147,645.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
3227 CHEADLE HALL SANTA BARBARA CA US 93106-0001 (805)893-4188 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
CA US 93106-5110 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Info Integration & Informatics |
| Primary Program Source: |
01001415DB NSF RESEARCH & RELATED ACTIVIT 01001516DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Online social networks (OSNs) such as Facebook and LinkedIn are valuable infrastructures for communication and interactions between a large volume of Internet users. For years, researchers have been trying to answer fundamental questions about the formation of these complex networks, their ongoing evolution, formation of internal structures, and change at different time scales. Since answering these questions requires real dynamics datasets at scale, most prior studies have been significantly constrained by a lack of data. The Principal Investigators have been granted access by an OSN provider to a uniquely detailed and complete trace of dynamics over 2+ years of a social network. The goal is to mine and analyze the traces of network dynamics to validate existing models and guide new models for fine grain network dynamics. Objectives include analysis of the preferential attachment model at different stages of network growth, developing new models of network dynamics at fine granularity in both time and graph topology, and explorations of applications driven by novel metrics of graph dynamics.
The work has the potential to dramatically change our understanding of dynamics in online social networks. By taking an empirical, data-driven approach to network modeling, they can shed light on how traditional models of network dynamics deviate from ground truth. In addition, they are developing empirical models that are more effective at accurately predicting network events at small scales. Both PIs Zhao and Zheng are heavily invested in educational and outreach programs for female and minority students: female students and postdocs often outnumber male counterparts in their lab. The PIs will disseminate their results to their collaborators atRenren and LinkedIn, and also share results with researchers at Twitter, Zynga, Facebook and Google through existing technical contacts and informal visits/talks. For further information, please see the project webpage (http://sandlab.cs.uchicago.edu/dynamics/).
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The main goal of this project is to study and improve our understanding of dynamics in online social networks through data mining of detailed, time stamped traces of network dynamics.
We tackled this problem in three directions. First, we performed detailed analysis on the presence of statistical scaling properties (self similarity), which is critical for determining how to model network dynamics. Second, by leveraging our access to several large, detailed traces of dynamics in online social networks (Facebook, Renren, YouTube), we performed detailed experiments to. reassess the value and accuracy of link prediction methods in dynamic networks. We only only understood the absolute and comparative accuracy of existing prediction algorithms, but also developed techniques to improve them using insights from analysis of network dynamics. Augmenting current algorithms with our proposed solution dramatically improves prediction accuracy across all traces and algorithms. Third, we also performed large-scale empirical analysis on several “emerging” online social networks, from digital payment systems (Venmo), online investment discussion board (Yahoo Finance), to streaming video markets. Using large, detailed traces in each of these networks, we obtained deep understanding on the impact of user behaviors and user interaction (e.g. communities) on the efficiency and growth of the network, and identified key challenges and vulnerabilities in these networks.
This research has in part supported 19 peer reviewed publications in top conference and journal venues. It has provided interdisciplinary training opportunities for 5 PhD students (3 female), 1 undergraduate, and 1 high school student (female). It has led to both domestic and international collaborations with researchers from Microsoft Research and Tsinghua University.
Last Modified: 01/24/2018
Modified by: Ben Y Zhao
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