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projects:year4:15.2

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15.2 - Predicting Future Relations: Incremental and Robust Link Prediction

Project - Summary

Objectives:

The project aims to develop and evaluate a robust link predicting methods incorporating ensemble and incremental learning capability. The objectives are to:

  • Implement multiple link prediction methods
  • Develop and evaluate the ensemble link prediction methods
  • Develop an incremental learning method and evaluate the performance of the model
  • Design and implement an interactive visualization system

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Vijay Raghavan PI Not available Not available UL Lafayette
Raju Gottumukkala PI Not available Not available UL Lafayette
Ryan Benton PI Not available Not available University of South Alabama
Murali K. Pusala Graduate Student Not available Not available UL Lafayette
Amirhossein Tavanaei Graduate Student Not available Not available UL Lafayette
Narendra Sanikommu Graduate Student Not available Not available UL Lafayette
Jaya Krishna Graduate Student Not available Not available UL Lafayette

Project - Impact and Uses/Benefits

Both of the methods that we proposed are implemented using C++ for high efficiency. Although the systems are tested using standard image and question-answer datasets, the evaluation can be extended to other datasets, potentially the ones from the IAB members.

We note that, the graph matching technique that we proposed in this project can also be adapted to other data types such as sound or video. Additionally, the graph embedding into HST can also be applied on clustering tasks which might appear fields such as in online marketing or social networks.

Project - Deep Dive

Project - Documents

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projects/year4/15.2.1566404163.txt.gz · Last modified: 2019/08/21 11:16 by sally.johnson