mrebollo MIguel Rebollo comunica2 complex networks

I belong to the Informatics Technology-Artificial Intelligence Group (GTI-IA). These are my research interests and the projects I am working on.


The topic of my Ph.D. in Artificial Intelligence (2004) was temporal knowledge representation and reasoning, directed by Vicent Botti and Eva Onaindía. The general idea was

  • to propose a temporal model to specify the beliefs of an intelligent agent, and
  • to create predictable methods to apply temporal reasoning in a strict real-time environment.

The dissertation is titled Real-time Imagination.

I continue working in the multiagent area, applying to reach agreements on autonomous entities and the dynamics of emergent, distributed complex systems. In the Publications section, you can see the main results achieved during these years.

In 2019, I completed a second Ph. D. in Complex Systems. It is entitled Generalization of Consensus Processes in Complex Networks, directed by Rosa M. Benito from the Complex System Group (GSC) at the Polytechnic University of Madrid (UPM). I am focused on complex networks and chaotic systems, and this is what I apply to social network analysis. It is particularly interesting when humans and artificial entities (agents) share and interact in the same environment. In that way, agents benefit from people’s knowledge, and people take advantage of the calculus capacity and analysis of intelligent agents.

And so the game went on. Our friend was absolutely correct: nobody from the group needed more than five links in the chain to reach, just by using the method of acquaintance, any inhabitant of our Planet

Chain Links, Frigyes Karinthy, 1929

Other research lines

Other projects arise From applying network analysis and artificial intelligence to other areas.

uTool: it began as a tool to extract tweets, and it has received more and more functionalities for network analysis, It also includes geolocated information and sentiment analysis, among others

Race analysis. The behavior of the participants in races can’t be explained as an isolated experience, and the rest of the runners in the neighborhood affect the performance. Considering the race as a dynamic complex network gives an insight into patterns that emerge and are common for all the races depending on the distance or the type of race (asphalt or trail):

Sentiment and topic analysis. of interactions on Twitter and other social networks.