| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | August 18, 2015 |
| Latest Amendment Date: | August 18, 2015 |
| Award Number: | 1526499 |
| Award Instrument: | Standard 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: | September 1, 2015 |
| End Date: | August 31, 2020 (Estimated) |
| Total Intended Award Amount: | $499,999.00 |
| Total Awarded Amount to Date: | $499,999.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
809 S MARSHFIELD AVE M/C 551 CHICAGO IL US 60612-4305 (312)996-2862 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
851 South Morgan Street Chicago IL US 60607-7053 |
| 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
Online social networks, such as Facebook, Twitter and Foursquare, have become increasingly popular in recent years. These online social networks contain abundant information about the users and their activities. Nowadays, to enjoy more social network services, people are getting involved in multiple social networks simultaneously. However, the accounts of the same user in different social networks are mostly isolated without any connection or correspondence to each other. This project has the potential to make fundamental, disruptive advances in fusion of heterogeneous networks for synergistic knowledge discovery. The success of this project will dramatically extend and change the current social network studies in data mining. In addition to social network analysis, this work can also be beneficial to scientific research such as life sciences on biological networks. The analytic tools developed and data collected will be made available to the public for free download.
The team will investigate the principles, methodologies and algorithms for the synergistic knowledge discovery across multiple partially aligned social networks, and evaluate the corresponding benefits. They plan to address the challenge on effective transfer of relevant knowledge across partially aligned networks, which will depend upon not only the relatedness of the different networks, but also the target application, e.g., link prediction vs clustering vs information diffusion. A general methodology will be developed, which will be shown to work for a diverse set of applications, while the specific parameter settings can be learned for each application using some training data. The problems studied include (1) Partial Network Alignment, (2) Integrated Anchor and Social Link Prediction, (3) Mutual Clustering, and (4) Cross-Networks Influence Maximization. This proposal will address these four major research problems systematically based on a unified concept: integrated anchor and social meta paths (including both intra- network and inter-network meta paths) for relationship exploration.
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.
Online social networks, such as Facebook, Twitter and Foursquare, have become increasingly popular in recent years. These online social networks contain abundant information about the users and their activities. Nowadays, to enjoy more social network services, people are getting involved in multiple social networks simultaneously. However, the accounts of the same user in different social networks are mostly isolated without any connection or correspondence to each other. We address the challenge on effective transfer of relevant knowledge across partially aligned networks, which depend upon not only the relatedness of the different networks, but also the target application, e.g., friend recommendation vs community detection vs information diffusion.
This work has demonstrated the general applicability and benefit of synergistic knowledge discovery across aligned social networks and developed unified principles, methods, and technologies for synergistic knowledge discovery over multiple applications across partially aligned heterogeneous social networks. It enriches the fundamental principles and technologies of social network mining and data mining. We address these major research problems systematically based on a unified concept of relationship exploration among the different network entities, including users, and postings, and fusion of information across different relationships, including both intra- network and inter-network relationships.
This work makes fundamental advances in fusion of heterogeneous networks for synergistic knowledge discovery. It extends and changes the current social network studies in data mining. In addition to social network analysis, this work can also be beneficial to broad learning, which focuses on fusing multiple large-scale information sources of diverse varieties together and carrying out synergistic data mining tasks across these fused sources, including scientific research such as life sciences on biological networks.
The PI is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data.
Last Modified: 09/02/2020
Modified by: Philip S Yu
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