Reticula: A temporal network and hypergraph analysis software package

Modiri, Arash Badie; Kivelä, Mikko

doi:10.1016/j.softx.2022.101301

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…of variation in hypergraphs, researchers are increasingly adapting concepts and methods from topology to analyse higher-order network data [13,62,63], and they have also made some progress by leveraging (structurally more constrained) simplicial complexes [64][65][66][67]. Recent surveys have consolidated our knowledge of when and how to use higher-order network models or collected the mathematical and computational tools currently available for their study [9,[68][69][70][71], and the software landscape for working with higher-order network data has improved dramatically over the past few years [72][73][74]. Nevertheless, the study of higher-order network data is still in its relative infancy, especially when compared with traditional network analysis.…”

Section: (B) Related Workmentioning

confidence: 99%

Legal hypergraphs

Coupette,

Hartung,

Katz

2024

Phil. Trans. R. Soc. A.

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Complexity science provides a powerful framework for understanding physical, biological and social systems, and network analysis is one of its principal tools. Since many complex systems exhibit multilateral interactions that change over time, in recent years, network scientists have become increasingly interested in modelling and measuring dynamic networks featuring higher-order relations . At the same time, while network analysis has been more widely adopted to investigate the structure and evolution of law as a complex system, the utility of dynamic higher-order networks in the legal domain has remained largely unexplored. Setting out to change this, we introduce temporal hypergraphs as a powerful tool for studying legal network data. Temporal hypergraphs generalize static graphs by (i) allowing any number of nodes to participate in an edge and (ii) permitting nodes or edges to be added, modified or deleted. We describe models and methods to explore legal hypergraphs that evolve over time and elucidate their benefits through case studies on legal citation and collaboration networks that change over a period of more than 70 years. Our work demonstrates the potential of dynamic higher-order networks for studying complex legal systems, and it facilitates further advances in legal network analysis. This article is part of the theme issue ‘A complexity science approach to law and governance’.

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Section: (B) Related Workmentioning

confidence: 99%

Legal hypergraphs

Coupette,

Hartung,

Katz

2024

Phil. Trans. R. Soc. A.

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“…There are several existing packages to represent and analyze higher-order networks: HyperNetX (Praggastis et al, 2023) and Reticula (Badie-Modiri & Kivelä, 2023) in Python, SimpleHypergraphs.jl (Spagnuolo et al, 2020) and HyperGraphs.jl (Diaz & Stumpf, 2022) in Julia, and hyperG in R. XGI is a valuable addition to the network science practitioner's toolbox for several reasons. First, XGI is implemented in pure Python, ensuring interoperability and easy installation across operating systems.…”

Section: Related Softwarementioning

confidence: 99%

XGI: A Python package for higher-order interaction networks

Landry¹,

Lucas²,

Iacopini³

et al. 2023

JOSS

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CompleX Group Interactions (XGI) is a library for analyzing higher-order networks. Such networks are used to model interactions of arbitrary size between entities in a complex system. This library provides methods for building hypergraphs and simplicial complexes; algorithms to analyze their structure, visualize them, and simulate dynamical processes on them; and a collection of higher-order datasets. XGI is implemented in pure Python and integrates with the rest of the Python scientific stack. XGI is designed and developed by network scientists with the needs of network scientists in mind.

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“…Phasik was originally designed to conduct the research presented in (Lucas et al, 2023). Phasik has also been compared to related software in (Badie-Modiri & Kivelä, 2023;Steer et al, 2023).…”

Section: Projects Using Phasikmentioning

confidence: 99%

Phasik: a Python package to identify system states in partially temporal networks

Lucas,

Townsend-Teague,

Neri

et al. 2023

JOSS

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Phasik is a Python library for analyzing the temporal structure of temporal and partially temporal networks. Temporal networks are used to model complex systems that consist of entities with time-varying interactions. This library provides methods for building temporal networks (including from data), visualizing them, and analyzing their structure. In particular, Phasik focuses on the identification of temporal phases, that is, periods of time during which the system is in a given state. The library supports partially temporal networks for which information about only a subset of the edges' temporal evolution is available. Phasik is implemented in pure Python and integrates with the rest of the Python scientific stack.

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Reticula: A temporal network and hypergraph analysis software package

Cited by 4 publications

References 26 publications

Legal hypergraphs

Legal hypergraphs

XGI: A Python package for higher-order interaction networks

Phasik: a Python package to identify system states in partially temporal networks

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