NDlib: a Python Library to Model and Analyze Diffusion Processes Over Complex Networks

Abstract

Nowadays the analysis of dynamics of and on networks represents a hot topic in the Social Network Analysis playground. To support students, teachers, developers and researchers in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.

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• The non-linear q-voter model
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• Latent geometry and dynamics of proximity networks
• From mean-field to complex topologies: network effects on the algorithmic bias model
• Hyperbolic Mapping of Human Proximity Networks
• Estimating the Expected Influence Capacities of Nodes in Complex Networks under the Susceptible-Infectious-Recovered (SIR) Model
• Network Diffusion -- Framework to Simulate Spreading Processes in Complex Networks