Every breakthrough begins with a question. Research gives that question a voice—and a path to discovery
This brief textbook explains the principles of social network analysis. The book goes beyond theoretical concepts and gives the reader complete knowledge about how to apply analytical techniques using Pajek to perform a large-scale network analysis. The book covers the topic in 2 sections – the first detailing fundamentals of research design and the next one about methods and applications. Readers can then apply the techniques in this book to other online communities, such as Facebook and Twitter.
This research monograph provides the means to learn the theory and practice of graph and network analysis using the Python programming language. The social network analysis techniques, included, will help readers to efficiently analyze social data from Twitter, Facebook, LiveJournal, GitHub and many others at three levels of depth: ego, group, and community. They will be able to analyse militant and revolutionary networks and candidate networks during elections. For instance, they will learn how the Ebola virus spread through communities.
I actively contribute to the scientific research community through work in computer science, artificial intelligence, and interdisciplinary applied research. My research focuses on medical imaging, smart systems, and machine learning, with the goal of turning advanced methods into practical tools that solve real-world problems.
I am especially interested in building AI systems that are accurate, usable, and trustworthy—with clear outputs, human oversight, and an emphasis on responsible use (privacy, transparency, and reliability).