Provides classes used for computing the results of the Papers
Unfolding Accessibility Provides a Macroscopic Approach to Temporal Networks,
Lentz et al., Phys. Rev. Lett. 110, 118701 (2013)
and
Disease Spread through Animal Movements: A Static and Temporal Network Analysis of Pig Trade in Germany, Lentz et al., PLOS ONE 11, e0155196–32 (2016)
Please cite the first reference (Phys. Rev. Lett.), if you use the software.
This software has been written for Python 3. If you are using Python 2, please choose the branch forPython2 on this website.
To compute the results of the papers, you only need the class AdjMatrixSequence. You can compute the Accessibility Matrix of a temporal network following the steps below.
For a first overview, use the Tutorial.ipynb notebook. The notebook works using the jupyter notebook command on your local machine. (If you want to see the online version and it does not render correctly, please open https://nbviewer.jupyter.org/ and paste the whole url of the notebook there).
To compute the Accessibility Matrix of a temporal network, follow the steps:
A = AdjMatrixSequence("<your input file>")This generates a list of adjacency matrices, i.e. a temporal network in matrices representation. The input file <your input file> is a simple text file with 3 columns and must be in the following form:
source_node target_node time
where the columns are separated by tabs. Thus, the file is a standard edge list with an additional time column. Examples are in the edgelists folder.
Additionally, more options could be used in this step. You can use option directed=True, if your network is directed (default is undirected). The option write_label_file=True (default is False) can be used, if you have nodes names that are not "matrix label friendly", as names for example. If write_label_file=True, an additional file is written into the working directory, which contains the old and new node labels.
c = A.unfold_accessibility()This computes eqn. (4) in the Paper, that is
$ \mathcal{P}_n = \bigwedge _i (\mathbf{1} \vee \mathbf{A}_i)$,
step by step and returns a dictionary containing the path density. In the paper [3] this is the black line in Fig. 2 for example.
Memory efficience. When using A.unfold_accessibility() like above, computations are done using matrices. These matrices can become huge for large networks. To save memory, you can use the following variant:
c = A.unfold_accessibility_memory_efficient()This variant can be slower, but works also for large networks. It decomposes the matrices into vectors and computes the solution for one vector at a time. (This method can be parallelized (to be done)).
h = np.gradient(c)This returns the numerical derivative of the path-density, which is the shortest-path-duration-distribution. In the paper [3] this is the red line in Fig. 2 for example.
Tools.dict2file(c, "path_density.txt")
Tools.dict2file(h, "path_durations.txt")This writes the generated data to txt-files, so you can plot it using gnuplot, Excel or you favorite plotting software. The files have 2 columns: time and path density or time and path duration, respectively.
A working example with steps 1—4 is shown in the file 'Unfold_Accessibility.py'.
A note on normalization. Path-density (c) and the distribution of shortest path durations (h) are not normalized at this point. Consequently, you should normalize c by the number of nodes squared and h to unity. As noted in the paper, c is not necessarily normalized to unity.
Causal fidelity is given by the causal path density normalized by the static path density. The causal path density is simply given by the last computed value c[-1]. The static path density can be computed using method At.static_path_density().
After c has been computed, the code would look like this:
causal_paths = c[-1]
static_paths = At.static_path_density()
print "---> Causal fidelity is ", float(causal_paths)/float(static_paths)The Class TemporalEdgeList provides methods to load a temporal network as a temporal edgelist. It can be used for randomization of temporal networks. There is a number of methods to randomize temporal networks. The methods implemented here have also been used in the supplementary material of [3] (also see references therein). The methods described in more detail described in my PhD thesis [4].
You can create a temporal edgelist like this:
E = TemporalEdgeList("<your input file>")You can add the option directed=True, if your network is directed. If you want to randomize the network, for example using the randomized edges (RE) model, you simply use
E.RE()Finally, you can write the new edgelist into a textfile like this:
E.write("Randomized_edges_RE.txt")- Python 3.6 (should run on other 3.x versions as well)
- scipy
- numpy
- Networkx package (optional, required for the configuration model).
The class AdjMatrixSequence is implemented using scipy.sparse matrices. In older Scipy versions (< 0.14.0), sparse matrices are restricted to 2^31 (~ 10^9) nonzero entries, regardless of the used memory. Make sure, your scipy version is up to date, if you plan to handle large networks (~ 10^5 nodes.)
Exemplary temporal network datasets are provided in the 'edgelists' folder. The file 'edgelists/sexual_contacts.dat' is from [1] and the file 'edgelists/sociopatterns_hypertext.dat' can be downloaded from sociopatterns.org [2]. Both files were also used in [3].
[1] L. E. C. Rocha, F. Liljeros, and P. Holme, Proc. Natl. Acad. Sci. U.S.a. 107, 5706 (2010).
[2] L. Isella, J. Stehle, A. Barrat, C. Cattuto, J.-F. Pinton, and W. Van den Broeck, J. Theor. Biol. 271, 166 (2011).
[3] H. H. K. Lentz, T. Selhorst, and I. M. Sokolov, Phys. Rev. Lett. 110, 118701 (2013).
[4] H. H. K. Lentz, PhD Thesis, Humboldt-University of Berlin or GitHub
[5] Lentz, H. H. K. et al., PLOS ONE 11, e0155196–32 (2016).