In the ever-evolving landscape of complex systems, the advent of new data has brought forth both opportunities and challenges. Among these challenges, the analysis of high-resolution temporal data stands out as a critical aspect, enabling a more nuanced understanding of temporal dynamics and their profound impact on complex systems. While recent research has prominently featured the exploration of temporal aspects in domains such as epidemics, the influence of time extends far beyond infectious diseases.

Temporal aspects permeate a myriad of complex systems, shaping their behaviour and evolution. For instance, in financial markets, the timely analysis of trading patterns can reveal valuable insights into market dynamics. Similarly, in ecological systems, understanding the temporal dynamics of species interactions is crucial for preserving biodiversity. The management of supply chains relies heavily on the precise synchronisation of events over time to optimise efficiency. Even social networks exhibit temporal patterns, with trends, information dissemination, and user interactions evolving over distinct temporal scales.

The workshop aims to foster the exchange of ideas and results centred around temporal aspects, providing a platform for experts from diverse domains to share their insights. By focusing on the temporal dimensions of complex systems, we seek to unravel patterns, models, and tools that enhance our comprehension and predictive capabilities. We invite submissions on a range of topics, including but not limited to:

• Research on Temporal Data: Investigating methodologies and techniques for effectively handling and analyzing temporal data in complex systems.

• Temporal Pattern Mining: Uncovering recurring patterns and trends within high-resolution temporal data to reveal underlying system dynamics.

• Models for Temporal Networks: Developing models that capture the temporal evolution of networks, shedding light on the intricate interplay between entities over time.

• Software Tools and Libraries: Showcasing innovative tools and libraries designed to facilitate the exploration and analysis of temporal aspects in complex systems.

• Machine Learning Applied to Complex Systems-Related Tasks: Exploring the application of machine learning algorithms to extract meaningful insights from temporal data within complex systems.

• Dynamics: Investigating the dynamic behavior of complex systems and the role of time in influencing their evolution.

• Network Evolution, Formation, Growth: Understanding how networks evolve and grow over time, elucidating the factors that drive their formation and development.

• Network Construction from Time Series: Proposing methodologies to construct meaningful networks based on temporal information, contributing to a comprehensive understanding of system dynamics.

• Machine Learning on Temporal Graphs: Harnessing machine learning techniques to analyse and derive patterns from temporal graph structures.

• Network Datasets with Temporal Information: Sharing datasets that encompass temporal information, fostering collaboration and benchmarking efforts within the research community.

Important dates

 Authors guidelines

Abstract papers: papers must not exceed 1 page, following the same guidelines for the main conference.

Submission:

• The submission form is available at the following link:

  • Submission Form 

Registration

• To join the satellite, registration to the CCS2024 main conference is mandatory, either for the full conference or for satellite events only. On site registration will be possible only upon availability.

Organisers