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Date: Tue, 21 Nov 2023 14:34:47 +0000 Subject: [PATCH 2/4] Creating 10.21105.joss.05872.crossref.xml --- joss.05872/10.21105.joss.05872.crossref.xml | 461 ++++++++++++++++++++ 1 file changed, 461 insertions(+) create mode 100644 joss.05872/10.21105.joss.05872.crossref.xml diff --git a/joss.05872/10.21105.joss.05872.crossref.xml b/joss.05872/10.21105.joss.05872.crossref.xml new file mode 100644 index 0000000000..b2573dad23 --- /dev/null +++ b/joss.05872/10.21105.joss.05872.crossref.xml @@ -0,0 +1,461 @@ + + + + 20231121T143412-0353a8130b647b25b380749509636e2e981dba03 + 20231121143412 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 11 + 2023 + + + 8 + + 91 + + + + Phasik: a Python package to identify system states in +partially temporal networks + + + + Maxime + Lucas + https://orcid.org/0000-0001-8087-2981 + + + Alex + Townsend-Teague + + + Matteo + Neri + https://orcid.org/0009-0007-0998-552X + + + Simone + Poetto + + + Arthur + Morris + + + Bianca + Habermann + https://orcid.org/0000-0002-2457-7504 + + + Laurent + Tichit + https://orcid.org/0000-0002-8350-1446 + + + + 11 + 21 + 2023 + + + 5872 + + + 10.21105/joss.05872 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10113244 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5872 + + + + 10.21105/joss.05872 + https://joss.theoj.org/papers/10.21105/joss.05872 + + + https://joss.theoj.org/papers/10.21105/joss.05872.pdf + + + + + + Empirical temporal networks of face-to-face +human interactions + Barrat + The European Physical Journal Special +Topics + 222 + 10.1140/epjst/e2013-01927-7 + 2013 + Barrat, A., Cattuto, C., Colizza, V., +Gesualdo, F., Isella, L., Pandolfi, E., Pinton, J.-F., Ravà, L., Rizzo, +C., Romano, M., & others. (2013). Empirical temporal networks of +face-to-face human interactions. The European Physical Journal Special +Topics, 222, 1295–1309. +https://doi.org/10.1140/epjst/e2013-01927-7 + + + Flow stability for dynamic community +detection + Bovet + Science Advances + 19 + 8 + 10.1126/sciadv.abj3063 + 2022 + Bovet, A., Delvenne, J.-C., & +Lambiotte, R. (2022). Flow stability for dynamic community detection. +Science Advances, 8(19), eabj3063. +https://doi.org/10.1126/sciadv.abj3063 + + + DyNetx: Dynamic network analysis +library + Rossetti + 10.5281/zenodo.3953119 + 2020 + Rossetti, G., bot, pyup.io, Utku +Norman, dormanh, & Dorner, M. (2020). DyNetx: Dynamic network +analysis library (Version v0.2.1). Zenodo. +https://doi.org/10.5281/zenodo.3953119 + + + Detecting social (in) stability in primates +from their temporal co-presence network + Gelardi + Animal Behaviour + 157 + 10.1016/j.anbehav.2019.09.011 + 2019 + Gelardi, V., Fagot, J., Barrat, A., +& Claidière, N. (2019). Detecting social (in) stability in primates +from their temporal co-presence network. Animal Behaviour, 157, 239–254. +https://doi.org/10.1016/j.anbehav.2019.09.011 + + + The synchronized dynamics of time-varying +networks + Ghosh + Physics Reports + 949 + 10.1016/j.physrep.2021.10.006 + 2022 + Ghosh, D., Frasca, M., Rizzo, A., +Majhi, S., Rakshit, S., Alfaro-Bittner, K., & Boccaletti, S. (2022). +The synchronized dynamics of time-varying networks. Physics Reports, +949, 1–63. +https://doi.org/10.1016/j.physrep.2021.10.006 + + + The graph-tool Python library + Peixoto + figshare + 10.6084/m9.figshare.1164194 + 2014 + Peixoto, T. P. (2014). The graph-tool +Python library. Figshare. +https://doi.org/10.6084/m9.figshare.1164194 + + + Fast and principled simulations of the SIR +model on temporal networks + Holme + PLOS ONE + 2 + 16 + 10.1371/journal.pone.0246961 + 2021 + Holme, P. (2021). Fast and principled +simulations of the SIR model on temporal networks. PLOS ONE, 16(2), +e0246961. +https://doi.org/10.1371/journal.pone.0246961 + + + Temporal networks + Holme + Physics Reports + 3 + 519 + 10.1016/j.physrep.2012.03.001 + 2012 + Holme, P., & Saramäki, J. (2012). +Temporal networks. Physics Reports, 519(3), 97–125. +https://doi.org/10.1016/j.physrep.2012.03.001 + + + The igraph software package for complex +network research + Csardi + InterJournal + Complex Systems + 2006 + Csardi, G., & Nepusz, T. (2006). +The igraph software package for complex network research. InterJournal, +Complex Systems, 1695. https://igraph.org + + + Cell cycle regulted transcription in +yeast + Koch + Current Opinion in Cell +Biology + 3 + 6 + 10.1016/0955-0674(94)90039-6 + 1994 + Koch, C., & Nasmyth, K. (1994). +Cell cycle regulted transcription in yeast. Current Opinion in Cell +Biology, 6(3), 451–459. +https://doi.org/10.1016/0955-0674(94)90039-6 + + + Neighbourhood matching creates realistic +surrogate temporal networks + Longa + arXiv:2205.08820 + 10.48550/arXiv.2205.08820 + 2022 + Longa, A., Cencetti, G., Lehmann, S., +Passerini, A., & Lepri, B. (2022). Neighbourhood matching creates +realistic surrogate temporal networks. arXiv:2205.08820. +https://doi.org/10.48550/arXiv.2205.08820 + + + Cerebral states during sleep, as studied by +human brain potentials. + Loomis + Journal of Experimental +Psychology + 2 + 21 + 10.1037/h0057431 + 1937 + Loomis, A. L., Harvey, E. N., & +Hobart, G. A. (1937). Cerebral states during sleep, as studied by human +brain potentials. Journal of Experimental Psychology, 21(2), 127. +https://doi.org/10.1037/h0057431 + + + Desynchronization induced by time-varying +network + Lucas + Europhysics Letters + 5 + 121 + 10.1209/0295-5075/121/50008 + 2018 + Lucas, M., Fanelli, D., Carletti, T., +& Petit, J. (2018). Desynchronization induced by time-varying +network. Europhysics Letters, 121(5), 50008. +https://doi.org/10.1209/0295-5075/121/50008 + + + Inferring cell cycle phases from a partially +temporal network of protein interactions + Lucas + Cell Reports Methods + 10.1016/j.crmeth.2023.100397 + 2023 + Lucas, M., Morris, A., +Townsend-Teague, A., Tichit, L., Habermann, B. H., & Barrat, A. +(2023). Inferring cell cycle phases from a partially temporal network of +protein interactions. Cell Reports Methods, 100397. +https://doi.org/10.1016/j.crmeth.2023.100397 + + + Detecting sequences of system states in +temporal networks + Masuda + Scientific Reports + 1 + 9 + 10.1038/s41598-018-37534-2 + 2019 + Masuda, N., & Holme, P. (2019). +Detecting sequences of system states in temporal networks. Scientific +Reports, 9(1), 1–11. +https://doi.org/10.1038/s41598-018-37534-2 + + + ndtv: Network Dynamic Temporal +Visualizations + Bender-deMoll + 2016 + Bender-deMoll, S. (2016). ndtv: +Network Dynamic Temporal Visualizations (Version 0.10). +https://github.com/statnet/ndtv + + + Exploring network structure, dynamics, and +function using NetworkX + Hagberg + Proceedings of the 7th Python in Science +Conference + 2008 + Hagberg, A. A., Schult, D. A., & +Swart, P. J. (2008). Exploring network structure, dynamics, and function +using NetworkX. In G. Varoquaux, T. Vaught, & J. Millman (Eds.), +Proceedings of the 7th Python in Science Conference (pp. 11–15). +https://conference.scipy.org/proceedings/SciPy2008/paper_2/ + + + Analysis and visualisation of time series +data on networks with pathpy + Hackl + Companion proceedings of the web conference +2021 + 10.1145/3442442.3452052 + 2021 + Hackl, J., Scholtes, I., Petrović, L. +V., Perri, V., Verginer, L., & Gote, C. (2021). Analysis and +visualisation of time series data on networks with pathpy. Companion +Proceedings of the Web Conference 2021, 530–532. +https://doi.org/10.1145/3442442.3452052 + + + Dynamic core-periphery structure of +information sharing networks in entorhinal cortex and +hippocampus + Pedreschi + Network Neuroscience + 3 + 4 + 10.1162/netn_a_00142 + 2020 + Pedreschi, N., Bernard, C., Clawson, +W., Quilichini, P., Barrat, A., & Battaglia, D. (2020). Dynamic +core-periphery structure of information sharing networks in entorhinal +cortex and hippocampus. Network Neuroscience, 4(3), 946–975. +https://doi.org/10.1162/netn_a_00142 + + + Raphtory: The temporal graph engine for rust +and python + Steer + arXiv:2306.16309 + 10.48550/arXiv.2306.16309 + 2023 + Steer, B., Arnold, N., Ba, C. T., +Lambiotte, R., Yousaf, H., Jeub, L., Murariu, F., Kapoor, S., Rico, P., +Chan, R., & others. (2023). Raphtory: The temporal graph engine for +rust and python. arXiv:2306.16309. +https://doi.org/10.48550/arXiv.2306.16309 + + + Reticula: A temporal network and hypergraph +analysis software package + Badie-Modiri + SoftwareX + 21 + 10.1016/j.softx.2022.101301 + 2023 + Badie-Modiri, A., & Kivelä, M. +(2023). Reticula: A temporal network and hypergraph analysis software +package. SoftwareX, 21, 101301. +https://doi.org/10.1016/j.softx.2022.101301 + + + SocioPatterns: A collection of contacts +datasets + 2008 + SocioPatterns: A collection of +contacts datasets. (2008). +http://www.sociopatterns.org/datasets/ + + + tacoma: A Python library for TemporAl COntact +Modeling and Analysis + Maier + 2018 + Maier, B. F. (2018). tacoma: A Python +library for TemporAl COntact Modeling and Analysis. +https://github.com/benmaier/tacoma + + + Teneto: A Python library for Temporal Network +Tools + Thompson + 10.5281/zenodo.3626827 + 2020 + Thompson, W. H., granitz, Harlalka, +V., & lcandeago. (2020). Teneto: A Python library for Temporal +Network Tools (Version 0.5.0). Zenodo. +https://doi.org/10.5281/zenodo.3626827 + + + tnetwork: A Python software package to +manipulate temporal networks + Cazabet + 2019 + Cazabet, R. (2019). tnetwork: A +Python software package to manipulate temporal networks (Version 0.5.0). +https://github.com/Yquetzal/tnetwork/ + + + RandTempNet: A collection of Python script +for temporal networks + Génois + 2019 + Génois, M. (2019). RandTempNet: A +collection of Python script for temporal networks. +https://github.com/mgenois/RandTempNet + + + Introducing the novel cytoscape app TimeNexus +to analyze time-series data using temporal MultiLayer networks +(tMLNs) + Pierrelée + Scientific Reports + 1 + 11 + 10.1038/s41598-021-93128-5 + 2021 + Pierrelée, M., Reynders, A., Lopez, +F., Moqrich, A., Tichit, L., & Habermann, B. H. (2021). Introducing +the novel cytoscape app TimeNexus to analyze time-series data using +temporal MultiLayer networks (tMLNs). Scientific Reports, 11(1), 1–17. +https://doi.org/10.1038/s41598-021-93128-5 + + + + + + From 065de6832f4db8bba0d1ecec3bb2e808aa34107c Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Tue, 21 Nov 2023 14:34:47 +0000 Subject: [PATCH 3/4] Creating 10.21105.joss.05872.jats --- joss.05872/10.21105.joss.05872.jats | 812 ++++++++++++++++++++++++++++ 1 file changed, 812 insertions(+) create mode 100644 joss.05872/10.21105.joss.05872.jats diff --git a/joss.05872/10.21105.joss.05872.jats b/joss.05872/10.21105.joss.05872.jats new file mode 100644 index 0000000000..5ce8506b9b --- /dev/null +++ b/joss.05872/10.21105.joss.05872.jats @@ -0,0 +1,812 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5872 +10.21105/joss.05872 + +Phasik: a Python package to identify system states in +partially temporal networks + + + +https://orcid.org/0000-0001-8087-2981 + +Lucas +Maxime + + +* + + + +Townsend-Teague +Alex + + + + +https://orcid.org/0009-0007-0998-552X + +Neri +Matteo + + + + + + +Poetto +Simone + + + + + + +Morris +Arthur + + + + +https://orcid.org/0000-0002-2457-7504 + +Habermann +Bianca + + + + +https://orcid.org/0000-0002-8350-1446 + +Tichit +Laurent + + + + + +CENTAI Institute, Turin, Italy + + + + +Dahlem Center for Complex Quantum Systems, Freie +Universitat Berlin, 14195 Berlin, Germany + + + + +Institut de Neurosciences de la Timone UMR 7289, Aix +Marseille Université, CNRS, Marseille 13005, France + + + + +Center for Modern Interdisciplinary Technologies, Nicolaus +Copernicus University, Toruń, Poland + + + + +Theory of Condensed Matter Group, Cavendish Laboratory, +University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United +Kingdom + + + + +Aix Marseille University, CNRS, IBDM UMR 7288, Turing +Center for Living Systems, Marseille, France + + + + +Aix Marseille University, CNRS, I2M UMR 7373, Turing Center +for Living Systems, Marseille, France + + + + +* E-mail: + + +13 +8 +2017 + +8 +91 +5872 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +temporal networks + + + + + + Summary +

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.

+
+ + Statement of need +

Temporal networks allow to model a wide range of complex systems + (Holme + & Saramäki, 2012), and study both their structure + (Longa + et al., 2022) and dynamics + (Ghosh + et al., 2022; + Lucas + et al., 2018). As such, temporal networks are used by + scientists from different fields and backgrounds. To help + non-specialists apply temporal network theory, common and ready-to-use + computational tools are therefore crucial. For traditional static + networks, several Python libraries have had a big impact on the field. + Examples include igraph + (Csardi + & Nepusz, 2006), NetworkX + (Hagberg + et al., 2008), and graph-tool + (Peixoto, + 2014). Temporal networks, however, include time as an + additional dimension and thus require different approaches. There + exist several Python libraries for working with temporal networks, + each focusing on different aspects of temporal networks analysis. None + of these libraries, however, focuses on identifying the temporal + structure of a complex system, which is often intricately linked to + the system’s function. Systems for which the temporal structure is + crucial span many scientific fields, with example including the phases + and subphases of the cell cycle (biology) + (Koch + & Nasmyth, 1994), the five sleep stages the brain goes + through (medicine) + (Loomis + et al., 1937), or the phases of social dynamics a school goes + through in a day + (Masuda + & Holme, 2019). A popular repository for temporal network + data is SocioPatterns + (Barrat + et al., 2013; + SocioPatterns, + 2008). Very often, understanding the temporal structure of such + systems is a first step towards understanding the underlying + mechanisms at play. A handful of studies have applied temporal network + theory to identify these system states from data + (Gelardi + et al., 2019; + Masuda + & Holme, 2019; + Pedreschi + et al., 2020).

+
+ + Related software +

There exist a few Python libraries for analyzing temporal networks, + each with a different focus. Most of them provide a base + TemporalNetwork class, methods for manually + adding and removing temporal edges, and methods for calculating basic + quantities such as degree, snapshots of the temporal network at a + given time, or neighbors. Teneto + (Thompson + et al., 2020) is versatile: it provides different static + visualizations, temporal network measures, algorithms for community + detection, and has methods to help using neuroimaging data. Tacoma + (Maier, + 2018) is the most advanced in terms of visualizations, with + interactive dynamic plotting functions. It also provides functions to + simulate spreading processes. Some of its core routines are written in + C++, improving its speed. Reticula + (Badie-Modiri + & Kivelä, 2023) has a C++ backbone and is a general-purpose + package with several generative models and randomization algorithms. + Raphtory is also general-purpose and written in Rust for speed, yet + accessible as a Python library + (Steer + et al., 2023). DyNetX + (Rossetti + et al., 2020) provides a class for directed temporal networks + as well as a method to compute time-respecting paths. RandTempNet + (Génois, + 2019) is more specifically focused on temporal network + randomization algorithms. The flow_stability repository + (Bovet + et al., 2022) provides code for the detection of time-varying + communities in temporal networks, similarly to tnetwork + (Cazabet, + 2019). Pathpy + (Hackl + et al., 2021)] has a slightly different focus: it provides + tools to analyze higher-order path correlations (or memory) in + time-stamped data,

+

A few libraries also exist in other programming languages. In R, + ndtv + (Bender-deMoll, + 2016) focuses on the visualization of temporal networks, both + static and animated. In C, P. Holme provides a fast implementation of + the susceptible-infected-recovered (SIR) epidemic model on temporal + networks + (Holme, + 2021). TimeNexus + (Pierrelée + et al., 2021) is a general-purpose Cytoscape app for temporal + networks with emphasis on modeling gene expression data.

+

Similarly to these libraries, Phasik provides general classes, + measures, and visualizations functions. However, it distinguishes + itself by having a specific focus on the identification of temporal + phases, and system states, in complex systems for a range of + time-scales from (partially) temporal networks. Concretely, Phasik + provides a PartiallyTemporalNetwork class and + classes dedicated to the clustering of snapshots for the + identification of phases. Phasik also naturally handles unevenly + spaced time points, which can result from experimental data, + especially in biology. Phasik was initially designed with biological + networks in mind but the analysis pipeline it provides is general and + can be used on any other type of temporal data.

+
+ + Overview + +

The Phasik pipeline to identify phases. First, a + partially temporal network is built from data, such as interaction + data (a graph) and (node) time series. Second, the distances between + snapshots of the temporal network at different times are encoded in + a distance matrix, similar to a recurrence matrix. Finally, clusters + of snapshots are computed from the distance matrix. Each snapshot is + associated to a time point, so that the resulting clusters of + snapshots correspond to periods of time—–phases. In + this example, time series are available for only three out of four + nodes. Consequently, a partially temporal network is built, in which + the edges without temporal information are set to a constant weight. +

+ +
+

The library is composed of two core classes for temporal networks + (TemporalNetwork and its child class + PartiallyTemporalNetwork) as well as three + additional classes designed for the identification of phases + (DistanceMatrix, + ClusterSet, and + ClusterSets). Phasik’s workflow consists of + three main steps, as illustrated in + [fig:pipeline]. + First, we build a TemporalNetwork by combining + time series data to interaction data in the form of a network. Second, + we compute a DistanceMatrix which encodes the + distance between the instantaneous snapshot of the temporal network + between any two time points, similarly to a recurrence matrix. Third, + we partition the temporal network into phases, through its + DistanceMatrix, by clustering its snapshots. + One can compute a single partition, or several partitions + corresponding to different numbers of clusters.

+

This workflow is conveniently implemented through constructor + methods: at each step, the new class instance is built from that of + the previous step with “.from_*”-named methods. For example, the + second step takes the form + DM = pk.DistanceMatrix.from_temporal_network(TN), + where TN is a + TemporalNetwork (a distance metric also needs + to be specified).

+

The library provides several ways to build a + TemporalNetwork from data. Temporal information + is always required and can be of three formats: (1) temporal edges, + i.e. tuples (i, j, t, w) to represent an interaction between nodes i + and j and at time t with weight w, (2) time series relative to nodes + (see + [fig:pipeline]), + and (3) time series relative to edges. Node time series are + automatically combined to form edge time series: by default, two node + series are multiplied and normalized to form an edge time series + (other ways can be specified by the user). Interaction data in the + form of a graph can optionally be used as input. In some experimental + scenarios, temporal information is not available for all edges in a + graph. This then results in a + PartiallyTemporalNetwork, where edges without + temporal information are assigned a constant default weight of one. + Phasik provides basic functions to visualize, animate, and analyze the + resulting temporal network.

+

The DistanceMatrix can be computed after + specifying a distance metric (by default, Euclidean distance is used). + Finally, after specifying a clustering method with related parameters, + and a number of clusters to compute, one can compute a + ClusterSet (by default, hierarchical clustering + is used). If a range of numbers of clusters is given, the library + computes a CluserSets instead. At each step, + Phasik provides visualization functions that can be fine-tuned by the + user.

+
+ + Projects using Phasik +

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).

+
+ + Acknowledgements +

We thanks Alain Barrat for useful discussions during the + development of the library.

+
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