Bio-inspired Data and Network Science with PolyPhy

Tutorial at ALIFE 2024 (July 22-26)

This tutorial will cover PolyPhy, an emergent Python package for A-life simulation of optimal transport networks with data science applications.

PolyPhy is an open-source tool that implements the slime mold inspired methodology called Monte Carlo Physarum Machine (MCPM) designed for growing, reconstructing, and visualizing continuous transport networks in 2D and 3D. Originally designed to map the structure of the cosmic web, PolyPhy is applicable to other types of complex networks: roads, linguistic networks, neuronal networks, structural scaffoldings, and others. PolyPhy can also be used as a sandbox to study the dynamics of massive multi-agent systems with stochastic behavioral strategies. An overview of the methodology and its applications is available in here.

Agenda

The tutorial will cover the MCPM method, the design architecture of PolyPhy, and overview case studies demonstrating its data pipelines with application to different datasets: cosmic web, road networks, linguistic embeddings.

Participation

The tutorial is open to all ALIFE attendees. No registration is necessary. The participants will be able to follow on their own computers and reproduce the presented use cases with a premade code, or choose to develop their own use cases by supplying own data and/or modifying PolyPhy’s simulation kernels. The participants interested in developing their experiments further can choose to share their results in a public GitHub repository and summarize them in a collective research report which will be appropriately disseminated.

Interest Groups

data scientist, network scientist, complexity scientist, computational artist

Organizer

Dr-Ing Oskar Elek, University of California in Santa Cruz (Web, Email)