PPSN 2020 Workshop

Workshop on Evolutionary and Bio-inspired techniques for Social Network Analysis

Brief description of the workshop topics

Social Network Analysis and Mining research have experienced an exponential growth in recent years, their application to industry and society has attracted the interest of scientists and engineers from a wide variety of areas such as Data Science, Big Data, Artificial Intelligence, Mathematics, Physics, Sociology or Psychology, just to mention a few of them. One of the reasons of this increasing interest is due to this application domain offers a particularly fertile place to test and develop the most advanced techniques, which can be used to extract some valuable information from these Online Social Networks. Current open problems, as Community Detection on networks (static and dynamic), Analysis of Information Diffusion, Sentiment Analysis and Opinion mining, or Visualization, etc. are proposing new techniques to handle with the huge complexity of the information available. To tackle with this complexity, evolutionary algorithms (as those based on multi or many objectives, differential evolution, or evolutionary strategies), and Swarm algorithms (as Ant Colonies, Particle swarms, Artificial Bees or Bat algorithms, among many others), have been successfully applied to face the SNA problems. This workshop aims to be a cross-disciplinary place to bring together experts, researchers and practitioners from several communities including computer science, physics, mathematics, marketing, sociology, psychology, etc., to explore the use of Evolutionary and Bio-inspired models, techniques and algorithms to work with the data, knowledge, and patterns, usually not trivial or hidden, stored at Social Networks.

General Information

The scope of the workshop covers, but is not limited to, the following topics

  • Evolutionary and Bio-inspired community discovery and analysis in Social Networks
  • Evolutionary and Bio-inspired graph mining
  • Evolutionary and Bio-inspired search algorithms for Social Networks
  • Models and theories from Social Sciences and their application to SNA applications
  • Dynamic Community finding and discovery
  • Pattern representation and modelling for Social Networks
  • Pattern analysis for Social Networks
  • Anomaly detection in Social Networks
  • Network formation and evolution
  • Information spread and diffusion
  • Models of network dynamics and diffusion
  • Sentiment Analysis and Opinion Mining
  • Intelligent data analysis in Social Media
  • Evolutionary and Bio-inspired based applications of SNA: marketing, cybercrime, polarization, dark web, advertising, politics, etc

Potential target participants and audience

Mainly from computer science, physics and mathematics, and with a good potential attractive to other researchers from social sciences areas as: psychology, sociology, criminology, marketing, advertising, political science, etc.

Roughly approximated number of participants

Between 20 to 40 attendees

Rough estimate of the number of talks

It is expected a maximum number of 8 talks, which could be scheduled in a one full-day

Contact details

David Camacho

Technical University of Madrid, Spain
Email: david.camacho@upm.es
Phone: +34 910673589
Address: C/Alan Turing, s/n-28031 Madrid, Spain

David Camacho is currently working as Associate Professor with Departamento de Sistemas Informáticos at Universidad Politécnica de Madrid (Technical University of Madrid, Spain) and leads the Applied Intelligence and Data Analysis group (AIDA: http://aida.ii.uam.es). He has published more than 250 journals, books, and conference papers. His research interests include Data Mining, Evolutionary Computation (GA, GP), Swarm Intelligence (ACO, PSO, ABC), and Machine Learning (Clustering, Hidden Markov Models, Classification, Deep learning, Social Network Analysis (Community Finding problems, graph theory), among others. He’s currently serving as Associate Editor, or belongs to the Editorial Board, in several journals as Information Fusion, Evolutionary Intgelligence, He has been participated, and leaded (as PI or Coordinator) in more tan 40 research projects (national and international funded as H2020, ISPF, DG, Erasmus+, etc).

Javier del Ser

Tecnalia, Spain
Basque Center for Applied Mathematics(BCAM), Spain
University of the Basque Country (UPV/EHU), Spain.
Email: javier.delser@tecnalia.com

Javier Del Ser (M’07-SM’12) received his first PhD degree (cum laude) in Electrical Engineering from the University of Navarra (Spain) in 2006, and a second PhD degree (cum laude, extraordinary PhD prize) in Computational Intelligence from the University of Alcalá (Spain) in 2013. He is currently a Research Professor in Artificial Intelligence and leading scientist of the OPTIMA (Optimization, Modelling and Analytics) research area at TECNALIA, Spain. He is also an adjunct professor at the University of the Basque Country (UPV/EHU), an invited research fellow at the Basque Centre for Applied Mathematics(BCAM), an a senior AI advisor at the technological start-up SHERPA.AI..He is also the coordinator of the Joint Research Lab between TECNALIA, UPV/EHU and BCAM, and the director of the TECNALIA Chair in Artificial Intelligence implemented at the University of Granada (Spain). His research interests are in the design of Artificial Intelligence methods for data mining and optimization applied to problems emerging from Industry 4.0, Intelligent Transportation Systems, Smart Mobility, Logistics and Health, among others. He has published more than 280 scientific articles, co-supervised 10 Ph.D. theses, edited 7books, co-authored 9 patents and participated/led more than 40 research projects. He is an Associate Editor of tier-one journals from areas related to Data Science Artificial Intelligence, such as Information Fusion, Swarm and Evolutionary Computation and Cognitive Computation. He is an IEEE Senior Member and a recipient of the Bizkaia Talent prize for his research career.