REINFORCE 2024

The 1st International Workshop on Disaster Network Science for Building Resilient Communities

REINFORCE 2024 (disasteR nEtwork scIeNce FOr Resilient CommunitiEs)

Held in conjunction with the 2024 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

University of Calabria, Rende (CS), Calabria, Italy, September 3, 2024

Disaster Network Science is an emerging trans-disciplinary research area principally based on the theory of Complex Networks and focusing on (i) the modeling of dynamic and fast information flow, (i) the robustness of information flow over multi-layer information environments and (iii) the design of efficient and robust communication networks in early warning and disaster recovery scenarios. The theoretical foundations of this research area are the Complex Networks that together with the Graph Theory provide methods, formalisms and analytical techniques for conceptualizing the network structure of communication and transport infrastructures, and their evolution due to the time-evolving nature of the emergencies (e.g. floodings, earthquakes, pandemics, tsunamis, fire, natural disasters, terrorism, etc.). Disaster Network Science aims to integrate the different contexts (i.e., layers) where emergencies unfold: from infrastructural networks to communication systems, human behavior changes in response to external stressors and can be studied by means of Social Sciences, Social Network Analysis and Social Media. The workshop REINFORCE (disasteR nEtwork scIeNce FOr Resilient CommunitiEs) aims at integrating such research contexts to foster community participation in the design of solutions (community-in-the-loop) thus improving disaster preparedness and response. The research fields of Computational, and Information Sciences, together with Ad-hoc Communication Networks are also invited to contribute aiming at developing effective and efficient communication strategies when significant failures, service disruptions or intermittent connections affect the communication platforms. Research in Disaster Network Science will finally offer novel methodologies and insights in the design of interventions and communication networks that focus on the involvement of people in communities as participants in information gathering and dissemination in both the virtual and physical worlds. This experience-centered design will facilitate the transmission of early warnings or alerts thus improving disaster management and paving the way for better and more efficient operations in responses.
REINFORCE 2024 aims to bring together researchers and academics involved in Disaster Network Science to discuss the latest research developments, share experiences and information about this trans-disciplinary research area. The final goal is to demonstrate how Disaster Network Science can enhance network resilience in face of future disasters by providing timely, fast and possibly green solutions, and offer to policymakers novel insights for informed and strategic decision-making.

Organizers

Annalisa Socievole

Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR)

Floriano De Rango

DIMES Department - University of Calabria, Italy

Manlio De Domenico

Department of Physics and Astronomy "Galileo Galilei" - University of Padua, Italy

Raffaele Guarasci

Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR)

TOPICS

The workshop offers a new venue for researchers and academics to present and discuss their latest results on this research area which relies on the theoretical and empirical basis of Social Sciences, Information Analytics, Communication Networks, Social Network Analysis, Complex Networks and Artificial Intelligence. With its trans-disciplinary and creative structure, the workshop aims at receiving various types of contributions and ensure rich interactions.

Authors are invited to submit manuscripts that present original unpublished research papers including but not limited to:

state-of-the art studies
dynamic complex networks
community detection algorithms, community participatory networks in emergencies
multilayer and multiscale networks in early warning systems and disaster management
models for health emergencies (e.g., pandemics and epidemics)
AI, big data ontologies and analytics in early warning systems and disaster management
urgent computing
human-centered design (HCD) of dissemination models in disaster networks
visualization of disaster networks
machine learning/deep learning for early warning and disaster management
green AI techniques
crowdsourcing and social media for early warning and disaster management
distributed disaster networks and applications
communication network models
methods for predicting upcoming failures in critical infrastructures
anomaly detection and mitigation techniques
cyber-security methods
infodemics characterization and mitigation

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