Scalability and Efficiency in Graph Mining
Wagner Meira Jr,
Universidade Federal de Minas Gerais –
Despite significant research, graph mining remains a challenging task, due to characteristics such as its computational complexity and the large spectrum of models that may be mined. In this talk we discuss some of these challenges and focus on strategies targeted at two significant issues in relevant scenarios, such as social networks and bioinformatics. The first issue is scalability and we present some strategies not only for creating computationally scalable solutions, but also for developing them more easily. The second issue is the efficiency of the mining process, and we present new graph mining models as well as robust sampling strategies for them. We conclude by summarizing the lessons learned and presenting current trends.
Wagner Meira Jr. obtained his PhD from the University of Rochester in
1997 and is Full Professor at the Computer Science Department at Universidade Federal de Minas Gerais, Brazil. He has published more than
300 papers in top venues and is co-author of the book Data Mining and Analysis – Fundamental Concepts and Algorithms published by Cambridge University Press in 2014. His research focuses on scalability and efficiency of large scale parallel and distributed systems, from massively parallel to Internet-based platforms, and on data mining algorithms, their parallelization, and application to areas such as information retrieval, cybersecurity, bioinformatics, and e-governance.
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