Complex Network Analysis using AI algorithms

Camelia Chira

Babes Bolyai University of Cluj-Napoca, Romania


The network science field has witnessed a tremendous growth in recent years due to the increasing availability of massive datasets modelled using complex networks, the advances made in the field of Artificial Intelligence (AI) and the demonstrated successes of network-based theories in domains such as biology, engineering, health and social sciences. Networks provide powerful models for the analysis and understanding of complex phenomena, offering a new perspective on data by creating structures and links that can be visualized and analysed using specialized algorithms. Nevertheless, networks pose significant challenges related to system functionality through node interactions, understanding network processes and predicting behaviour.

This talk provides an introduction to the network science field, covering important tasks in complex network analysis, including the identification of important nodes in the network, outlining the cycles and relevant paths in the network, detection of communities and network dynamics. For each of these problems, relevant methods and AI algorithms will be presented and discussed based on real examples and results for different types of networks.

Short Bio:

Camelia Chira is professor in computer science at Babeş-Bolyai University (Romania), doctoral advisor and senior researcher in computational intelligence as part of the Metaheuristics for Complex Systems research group from Faculty of Mathematics and Computer Science. After graduating in computer science at Babeş-Bolyai University in 1998, Camelia pursued her Master and PhD studies at Galway-Mayo Institute of Technology (Ireland) in the area of agent-based systems for distributed collaborative design. Since 2005, Camelia Chira participated in several research projects and interdisciplinary collaborations with results being exploited in industry or published in journals and conferences. Research interests include computational intelligence, evolutionary algorithms, nature- inspired computing, complex networks, multi-agent systems and bioinformatics. Important results refer to the development of evolutionary algorithms for the optimization of urban bicycle renting systems, decision support systems for the analysis of mammography images, route optimization methods for electrical vehicles, gene clustering methods for microarray analysis and community detection algorithms for complex networks.