| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | January 5, 2012 |
| Latest Amendment Date: | February 8, 2016 |
| Award Number: | 1149837 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | January 15, 2012 |
| End Date: | December 31, 2017 (Estimated) |
| Total Intended Award Amount: | $540,728.00 |
| Total Awarded Amount to Date: | $540,728.00 |
| Funds Obligated to Date: |
FY 2013 = $93,025.00 FY 2014 = $96,068.00 FY 2015 = $99,214.00 FY 2016 = $102,469.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
450 JANE STANFORD WAY STANFORD CA US 94305-2004 (650)723-2300 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Stanford University Stanford CA US 94305-4100 |
| 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: |
01001314DB NSF RESEARCH & RELATED ACTIVIT 01001415DB NSF RESEARCH & RELATED ACTIVIT 01001516DB NSF RESEARCH & RELATED ACTIVIT 01001617DB 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
Social, technological, information and biological systems can be studied as graphs, where nodes represent entities (i.e., people, websites) and edges represent interactions (friendships, communication). The project aims to analyze and discover explanatory and predictive models of networked systems, such as large groups of people and societies, or large biological and technological systems, in order to understand their structure and make predictions about their global dynamics.
The research studies the structure and dynamics of communities of nodes, with the goal to invent novel network community detection methods and build predictive models of behavior of groups of nodes. The proposed research has three main thrusts: (1) Structure and discovery of network communities, (2) Dynamics and "health" of network communities, and (3) Supervised community detection in networks with rich node and edge metadata. The research focuses on harnessing massive network datasets, as certain behaviors and patterns are observable only when the amount of data is large enough. The intellectual focus of the project is on increasing the expressivity of the models to also include rich node and edge metadata and explore the connections between the network structure and the attributes/features of nodes and edges.
The education plan provides for rich research experiences and helps students develop the interdisciplinary attitudes and skills needed for this work through courses that look at real-world network problems and data. An integral part of this proposal is public release of datasets and computational tools for analysis of large networks. Additional information about the project including publications, data sets, source code, and educational materials can be accessed through the project website at http://snap.stanford.edu.
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 world around us is becoming more and more connected: Technological systems, such as, sensor networks, power grids and water distribution systems, and biological systems are all analyzed as networks. Humans are creating massive social and information networks on the Web. All these can be studied as graphs, where nodes represent entities (i.e., people, web sites) and edges represent interactions (friendships, communication).
Nodes in such networks organize into communities of nodes that share a common property, role or function, such as social communities, functionally related proteins, or topically related webpages. Identifying such communities – the building blocks of networks – is crucial to the understanding of the structural and functional roles of networks. Communities in networks often overlap in the sense that a node can belong to multiple communities.
This project was conducted to understand what is the structure of networks and how nodes in networks organize into clusters or communities. The work has three main practical outcomes beyond proposed advances in academic research: 1) to develop novel methods that allow us to extract knowledge about the organization of complex networks, 2) to model, measure and improve our understanding of networks, and 3) to improve social media and social networking applications.
Applications of the developed research were carried out in close collaboration with partners from academia and industry. The project also provided for rich educational and research experiences and helped students develop the interdisciplinary attitudes and skills through courses. An integral part of this research was also public release of datasets and tools for analysis of large networks.
Last Modified: 05/16/2018
Modified by: Jurij Leskovec
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