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Drift detection in graph streams

   Department of Computer Science

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Graphs have become a useful tool for representing information in many application domains. Social, computer, sensor and transport networks, molecular structures and business processes, all can be represented as attributed graphs. One of the characteristics of such graphs is dynamism – the graph structure, as well as the attributes of nodes and edges can change over time. The accuracy of predictive and inference models built over dynamic graphs depends on the ability of the models to adapt to these changes. This project will propose novel methods for detecting changes in graphs over time (also known as drifts) and demonstrate their usefulness in downstream machine learning tasks performed over dynamic graphs. 


You must have a good Bachelor's degree (2.1 or higher, or equivalent) or Master's degree in Computer Science, Mathematics or a similar relevant subject.

Experience with machine learning and programming in Python are essential. Knowledge of graph analysis, deep learning, graph embedding and stream mining techniques are desirable. 

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