Complex Methods Applied to Data Analysis, Processing, and Visualisation

The amount of data available every day is not only enormous but growing at an exponential rate. Over the last ten years there has been an increasing interest in using complex methods to analyse and visualise massive datasets, gathered from very different sources and including many different features: social networks, surveillance systems, smart cities, medical diagnosis systems, business information, cyberphysical systems, and digital media data. Nowadays, there are a large number of researchers working in complex methods to process, analyse, and visualise all this information, which can be applied to a wide variety of open problems in different domains. This special issue presents a collection of research papers addressing theoretical, methodological, and practical aspects of data processing, focusing on algorithms that use complex methods (e.g., chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory) in a variety of domains (e.g., software engineering, digital media data, bioinformatics, health care, imaging and video, social networks, and natural language processing). A total of 27 papers were received from different research fields, but sharing a common feature: they presented complex systems that process, analyse, and visualise large amounts of data. After the review process, 8 papers were accepted for publication (around 30% of acceptance ratio).


Volume 2019, Article ID 9316123, 2 pages
Complex Methods Applied to Data Analysis, Processing, and Visualisation
Jose Garcia-Rodriguez, Anastasia Angelopoulou, David Tomás, and Andrew Lewis


Complexity in Forecasting and Predictive Models

The challenge of this special issue has been to know the state of the problem related to forecasting modeling and the creation of a model to forecast the future behavior that supports decision making by supporting real-world applications.

This issue has been highlighted by the quality of its research work on the critical importance of advanced analytical methods, such as neural networks, soft computing, evolutionary algorithms, chaotic models, cellular automata, agent-based models, and finite mixture minimum squares (FIMIX-PLS)

Mainly, all the papers are focused on triggering a substantive discussion on how the model predictions can face the challenges around the complexity field that lie ahead. These works help to better understand the new trends in computing and statistical techniques that allow us to make better forecasts. Complexity plays a prominent role in these trends, given the increasing variety and changing data flows, forcing academics to adopt innovative and hybrid methods.


Volume 2019, Article ID 8160659, 3 pages
Complexity in Forecasting and Predictive Models
Jose L. Salmeron, Marisol B. Correia, and Pedro R. Palos-Sanchez



On complexity of branching droplets in electrical field

Decanol droplets in a thin layer of sodium decanoate with sodium chloride exhibit bifurcation branching growth due to interplay between osmotic pressure, diffusion and surface tension. We aimed to evaluate if morphology of the branching droplets changes when the droplets are subject to electrical potential difference. We analysed graph-theoretic structure of the droplets and applied several complexity measures. We found that, in overall, the current increases complexity of the branching droplets in terms of number of connected components and nodes in their graph presentations, morphological complexity and compressibility.


On complexity of branching droplets in electrical field
Mohammad Mahdi Dehshibi, Jitka Cejkova, Dominik Svara, Andrew Adamatzky


A simple contagion process describes spreading of traffic jams in urban networks

The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two novel macroscopic characteristics of network traffic, namely congestion propagation rate \b{eta} and congestion dissipation rate {\mu}. We describe the dynamics of congestion propagation and dissipation using these new parameters, \b{eta}, and {\mu}, embedded within a system of ordinary differential equations, analogous to the well-known Susceptible-Infected-Recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.


A simple contagion process describes spreading of traffic jams in urban networks
Meead Saberi, Mudabber Ashfaq, Homayoun Hamedmoghadam, Seyed Amir Hosseini, Ziyuan Gu, Sajjad Shafiei, Divya J. Nair, Vinayak Dixit, Lauren Gardner, S. Travis Waller, Marta C. González


Machine Learning and Modeling at CSS’2019

The science of complex systems provides the framework for understanding patterns of behavior, and their emergence, at multiple scales in social and other types of systems. The analytical toolsets provided by AI and Machine Learning are good to recognize and measure such patterns in the data. The combination of pattern recognition and generation mechanisms provides an opportunity to advance our understanding of the complexity of real systems. Ultimately, we could benefit from such complexity, rather than being endangered by it, design better technologies, decisions and strategies.

  • Show new ways to model complex and social systems by means of big data analysis, machine learning and AI.
  • Explore new ways to analyze the data, taking into account the complexity of underlying systems.
  • We would like to address how to formulate the right questions and retrieve the relevant information.

The opportunities available from big data and machine learning could solve challenging problems but we must analyze and interpret the data properly. Wrong assumptions and simplified views could separate modeling from reality. We expect to raise awareness about interventions in complex systems, the risk we face when societies become global, the opportunities that are created, and the role of complexity in data analytics.