354 lines
12 KiB
XML
Executable File
354 lines
12 KiB
XML
Executable File
<!--
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SPDX-FileCopyrightText: 2015-2023 Tanguy Fardet
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SPDX-License-Identifier: CC0-1.0
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-->
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<input>
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<!-- Test suites -->
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<test name="test_graphclasses">
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<graph_list>
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<name>from_scipy_sparse_rand</name>
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<name>from_numpy_randint</name>
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</graph_list>
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</test>
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<test name="test_generation">
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<graph_list>
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<name>fixed_indegree</name>
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<name>fixed_outdegree</name>
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<name>gaussian_indegree</name>
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<name>gaussian_outdegree</name>
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<!--
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<name>erdos_sparse</name>
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<name>erdos_dense</name>
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<name>erdos_recip</name>
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<name>rsf_2.2_2.2</name>
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<name>rsf_2.1_2.9</name>
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<name>rsf_2.9_2.1</name>
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<name>rsf_2.2_recip</name>
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<name>newman_watts</name>
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-->
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<name>linear_distance_rule</name>
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<name>exponential_distance_rule</name>
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</graph_list>
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</test>
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<test name="test_attributes">
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<graph_list>
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<name>erdos_uniform_weights</name>
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<name>erdos_gaussian_weights</name>
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<name>erdos_lognormal_weights</name>
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<name>erdos_lincorrbetw_weights</name>
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</graph_list>
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</test>
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<test name="test_io">
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<graph_list>
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<name>small_linear_distance_rule</name>
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<name>small_erdos_lognormal_weights</name>
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<name>p2p-Gnutella04.txt</name>
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<name>opsahl-usairport.el</name>
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<name>karate.gml</name>
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<name>polbooks.gml</name>
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<name>rat_brain.graphml</name>
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</graph_list>
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</test>
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<test name="test_mpi">
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<graph_list>
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<name>linear_distance_rule</name>
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<name>exponential_distance_rule</name>
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</graph_list>
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</test>
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<!-- Graphs -->
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<graphs>
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<!-- Reference graphs -->
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<graph name="p2p-Gnutella04.txt">
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<load_options>
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<string name="fmt">edge_list</string>
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<string name="separator">\t</string>
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</load_options>
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<int name="nodes">10879</int>
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<int name="edges">39994</int>
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<int name="diameter">5.4</int> <!-- must be greater than that -->
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<int name="nodes_largest_wcc">10876</int>
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<int name="edges_largest_wcc">39994</int>
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<int name="nodes_largest_scc">4317</int>
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<int name="edges_largest_scc">18742</int>
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<float name="clustering">0.0062</float>
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<bool name="directed">True</bool>
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</graph>
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<graph name="opsahl-usairport.el">
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<load_options>
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<string name="fmt">edge_list</string>
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<string name="ignore">%</string>
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<string name="separator"> </string>
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</load_options>
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<int name="nodes">1574</int>
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<int name="edges">28236</int>
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<bool name="directed">True</bool>
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</graph>
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<graph name="karate.gml">
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<load_options>
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<string name="fmt">gml</string>
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</load_options>
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<int name="nodes">34</int>
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<int name="edges">78</int>
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<bool name="directed">True</bool>
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</graph>
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<graph name="polbooks.gml">
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<load_options>
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<string name="fmt">gml</string>
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</load_options>
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<int name="nodes">105</int>
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<int name="edges">441</int>
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<bool name="directed">False</bool>
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</graph>
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<graph name="rat_brain.graphml">
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<int name="nodes">503</int>
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<int name="edges">27667</int>
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<bool name="directed">True</bool>
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</graph>
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<!-- testing graph generation -->
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<graph name="fixed_indegree">
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<generate_options>
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<string name="graph_type">fixed_degree</string>
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<int name="nodes">5000</int>
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<int name="degree">100</int>
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<string name="degree_type">in</string>
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</generate_options>
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</graph>
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<graph name="fixed_outdegree">
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<generate_options>
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<string name="graph_type">fixed_degree</string>
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<int name="nodes">5000</int>
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<int name="degree">100</int>
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<string name="degree_type">out</string>
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</generate_options>
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</graph>
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<graph name="gaussian_indegree">
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<generate_options>
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<string name="graph_type">gaussian_degree</string>
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<int name="nodes">5000</int>
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<int name="avg">100</int>
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<float name="std">5.</float>
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<string name="degree_type">in</string>
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</generate_options>
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</graph>
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<graph name="gaussian_outdegree">
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<generate_options>
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<string name="graph_type">gaussian_degree</string>
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<int name="nodes">5000</int>
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<int name="avg">100</int>
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<float name="std">5.</float>
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<string name="degree_type">out</string>
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</generate_options>
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</graph>
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<graph name="erdos_sparse">
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<generate_options>
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<string name="graph_type">erdos_renyi</string>
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<int name="nodes">1000</int>
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<float name="density">0.04</float>
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</generate_options>
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</graph>
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<graph name="erdos_dense">
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<generate_options>
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<string name="graph_type">erdos_renyi</string>
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<int name="nodes">1000</int>
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<float name="density">0.2</float>
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</generate_options>
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</graph>
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<graph name="erdos_recip">
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<generate_options>
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<string name="graph_type">erdos_renyi</string>
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<int name="nodes">1000</int>
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<float name="density">0.06</float>
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<float name="reciprocity">0.2</float>
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</generate_options>
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</graph>
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<graph name="rsf_2.2_2.2">
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<generate_options>
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<string name="graph_type">random_scale_free</string>
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<int name="nodes">1000</int>
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<float name="in_exp">2.2</float>
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<float name="out_exp">2.2</float>
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</generate_options>
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</graph>
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<graph name="rsf_2.1_2.9">
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<generate_options>
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<string name="graph_type">random_scale_free</string>
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<int name="nodes">1000</int>
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<float name="in_exp">2.1</float>
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<float name="out_exp">2.9</float>
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</generate_options>
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</graph>
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<graph name="rsf_2.9_2.1">
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<generate_options>
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<string name="graph_type">random_scale_free</string>
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<int name="nodes">1000</int>
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<float name="in_exp">2.9</float>
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<float name="out_exp">2.1</float>
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</generate_options>
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</graph>
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<graph name="rsf_2.2_recip">
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<generate_options>
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<string name="graph_type">random_scale_free</string>
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<int name="nodes">1000</int>
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<float name="in_exp">2.2</float>
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<float name="out_exp">2.2</float>
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<float name="reciprocity">0.3</float>
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</generate_options>
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</graph>
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<graph name="newman_watts">
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<generate_options>
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<string name="graph_type">newman_watts</string>
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<int name="nodes">1000</int>
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<int name="coord_nb">20</int>
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<float name="proba_shortcut">0.1</float>
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</generate_options>
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</graph>
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<graph name="linear_distance_rule">
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<generate_options>
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<string name="graph_type">distance_rule</string>
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<int name="nodes">5000</int>
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<float name="scale">500</float>
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<string name="rule">lin</string>
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<int name="avg_deg">100</int>
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<float name="neuron_density">1000.</float>
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</generate_options>
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</graph>
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<graph name="exponential_distance_rule">
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<generate_options>
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<string name="graph_type">distance_rule</string>
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<int name="nodes">5000</int>
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<float name="scale">130</float>
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<string name="rule">exp</string>
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<int name="avg_deg">100</int>
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<float name="neuron_density">1000.</float>
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</generate_options>
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</graph>
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<graph name="from_scipy_sparse_rand">
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<generate_options>
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<int name="m">10</int>
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<int name="n">10</int>
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<float name="density">0.1</float>
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<string name="format">csr</string>
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</generate_options>
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</graph>
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<graph name="from_numpy_randint">
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<generate_options>
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<int name="low">0</int>
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<int name="high">3</int>
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<list name="size" type="int">(10, 10)</list>
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</generate_options>
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</graph>
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<!-- testing attributes and distributions -->
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<graph name="erdos_uniform_weights">
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<generate_options>
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<string name="graph_type">erdos_renyi</string>
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<int name="nodes">2000</int>
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<float name="density">0.04</float>
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<weights>
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<string name="distribution">uniform</string>
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<float name="lower">0.02</float>
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<float name="upper">3.</float>
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</weights>
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</generate_options>
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</graph>
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<graph name="erdos_gaussian_weights">
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<generate_options>
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<string name="graph_type">erdos_renyi</string>
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<int name="nodes">2000</int>
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<float name="density">0.04</float>
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<weights>
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<string name="distribution">gaussian</string>
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<float name="avg">2.</float>
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<float name="std">0.3</float>
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</weights>
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</generate_options>
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</graph>
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<graph name="erdos_lognormal_weights">
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<generate_options>
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<string name="graph_type">erdos_renyi</string>
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<int name="nodes">2000</int>
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<float name="density">0.04</float>
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<weights>
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<string name="distribution">lognormal</string>
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<float name="position">2.</float>
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<float name="scale">0.3</float>
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</weights>
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</generate_options>
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</graph>
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<graph name="erdos_lincorrbetw_weights">
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<generate_options>
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<string name="graph_type">erdos_renyi</string>
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<int name="nodes">1000</int>
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<float name="density">0.04</float>
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<weights>
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<string name="distribution">lin_corr</string>
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<string name="correl_attribute">betweenness</string>
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<string name="btype">edge</string>
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<bool name="weights">False</bool>
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<float name="lower">0.001</float>
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<float name="upper">5.0</float>
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</weights>
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</generate_options>
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</graph>
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<graph name="small_erdos_lognormal_weights">
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<generate_options>
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<string name="graph_type">erdos_renyi</string>
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<int name="nodes">100</int>
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<float name="density">0.1</float>
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<weights>
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<string name="distribution">lognormal</string>
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<float name="position">2.</float>
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<float name="scale">0.3</float>
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</weights>
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</generate_options>
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</graph>
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<graph name="small_linear_distance_rule">
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<generate_options>
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<string name="graph_type">distance_rule</string>
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<int name="nodes">100</int>
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<float name="scale">200</float>
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<string name="rule">lin</string>
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<int name="avg_deg">10</int>
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</generate_options>
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</graph>
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</graphs>
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</input>
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