What is it about?
To understand phenomena such as opinion dynamics or infectious diseases, scientists use computer simulations. In this work, we present a Python toolkit: "Network Diffusion", that can help researchers in that task. Its uniqueness is the ability to work with both temporal and multilayer networks.
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Why is it important?
Our paper presents several use cases that can be modelled with "Network Diffusion", particularly from the domain of multilayer networks, temporal networks and various diffusion processes (opinion, contagion, social learning). It also shows how easy it is to build a new spreading model from provided interfaces.
Perspectives
This work is aimed at helping researchers from the domain of network science speed up their experiments by having an engine to build up their target environment quickly.
Michał Czuba
Politechnika Wroclawska
Read the Original
This page is a summary of: Network Diffusion Framework to Simulate Spreading Processes in Complex Networks, Big Data Mining and Analytics, September 2024, Tsinghua University Press,
DOI: 10.26599/bdma.2024.9020010.
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