| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | June 21, 2016 |
| Latest Amendment Date: | November 30, 2018 |
| Award Number: | 1639792 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Wei Ding
IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | July 15, 2016 |
| End Date: | June 30, 2019 (Estimated) |
| Total Intended Award Amount: | $200,000.00 |
| Total Awarded Amount to Date: | $200,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
926 DALNEY ST NW ATLANTA GA US 30318-6395 (404)894-4819 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
225 North Avenue Atlanta GA US 30332-0002 |
| 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: |
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| 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
The purpose of this research project is to develop probabilistic models and the related machine learning algorithms for modeling network evolution and dynamics. The research lays theoretic foundations and provides practical tools for scientists to control networks in order to achieve desirable outcomes. Although the research is widely applicable, the research team primarily considers two application areas: social networks and P2P microfinance. In social networks, this project brings practical values to the Internet industry by better understanding and modeling of user behaviors and their impacts on social ties and social group formation. For P2P microfinance, this project has the potential to better engage not-for-profit lenders and thus to help small business in developing countries. Furthermore, the research provides materials and contents for both undergraduate and graduate education and helps students develop interdisciplinary mindsets and tools needed to tackle real-world problems.
This proposed research aims to develop machine learning theory and algorithms for networked asynchronous and interdependent event streams arising from modern applications. The researchers especially emphasize methodology that can handle temporal networks when the underlying network structures are undergoing substantial changes. One major theme of the proposal is the modeling of the interplay between network node dynamics and network topology dynamics, or network co-evolution. The researchers propose a novel framework based on multivariate point processes for modeling and analyzing event data. The methods significantly expand the application area of conventional machine learning techniques. One example is to answer the question ``who will do what and when'', which is critical to event sequence modeling in network data analysis where traditional machine learning algorithms are difficult to apply.
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.
When we describe the behaviors of agents using event sequences, for example when certain person posts a message or retweet a message, it is also essential for characterizing the behavior of the population of agents that we also model the interactions among the agents themselves which form an evolving network. More importantly we want to understand the interactions between these two types of dynamics: dynamics of the network and dynamics on the network. This research project explores temporal point process methodology and deep neural network methods to develop algorithms to address the above problems. Specifically, we developed temporal point process models that can capture the richness of the marks specifying the types of events, and we also developed scalable algorithms in the form of decoupled training to handle the exponential size of the mark space; We explore sequence to sequence models in order to overcome the limitations of parametric point process models when addressing the problem of error propagation in event prediction problems; We also developed a new modeling framework named DyRep using a temporal-attentive representation network to encode temporally evolving structural information into node representations which in turn drives the nonlinear evolution of the observed network dynamics. All three methods combined provide a solid foundation we can build on to further explore network co-evolution problems which have diverse applications.
Last Modified: 10/01/2019
Modified by: Duen Horng Chau
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