1 Walshaw
Note: We do not provide the the data ourselves. Walshaw requests to be emailed for the distribution of the data, and so we link to their site and to the graphs found in the SuiteSparse Matrix Collection. Origin Notes: C. Walshaw, collected for an archive of graph partitions which has been maintained since 2000. These are multilevel mesh partitions of heterogeneous networks from a variety of domains. graph features handled: Mesh, Partition, Weighted edges, Weighted nodes Graph features in papers: clusters (generated),generic,generic,generic,large,generic,high degree,large,dynamic,multilevel,large Origin Paper: A Combined Evolutionary Search and Multilevel Optimisation Approach to Graph Partitioning (https://www.notion.so/A-Combined-Evolutionary-Search-and-Multilevel-Optimisation-Approach-to-Graph-Partitioning-d58eded005d14e428a11b9ca02c64eb1?pvs=21), Mesh Partitioning: a Multilevel Balancing and Refinement Algorithm (https://www.notion.so/Mesh-Partitioning-a-Multilevel-Balancing-and-Refinement-Algorithm-97fff4a9a42b469cbd16a74d0a4aa48f?pvs=21) Originally found at: https://chriswalshaw.co.uk/partition/ where C. Walshaw requests to be emailed for the dataset at mailto:c.walshaw@gre.ac.uk. the individual graphs can also be found on the SuiteSparse Matrix Collection under different groups.
Size: 34 graphs, 2395-448695 nodes, 6837-3314611 edges. Node-weighted, edge-weighted. Partitions of the 34 graphs are given with various partition sizes and imbalance percentages. Child collections: SuiteSparse Matrix Collection (SuiteSparse%20Matrix%20Collection%20b8772d6a2cbb456894b4673e32c6f956.md), Scotch Graph Collection (Scotch%20Graph%20Collection%20cfe6f105da3a4c699fa3e02ca1f6e88a.md) Appeared in years: 2002,2007,2005,2006,2004,2017,2008,2019,2020 Type of Collection: Subset of other collection is it stored properly?: No must be analyzed: Yes In repo?: No Related to Literature - Algorithm (1) (Dataset tag relations): Efficient and High Quality Force-Directed Graph Drawing (https://www.notion.so/Efficient-and-High-Quality-Force-Directed-Graph-Drawing-0690316942ef4c97bec1a11b26255410?pvs=21), Drawing graphs by eigenvectors: theory and practice (https://www.notion.so/Drawing-graphs-by-eigenvectors-theory-and-practice-1b5e3919d8c94ad18f29f20c7480b07a?pvs=21), Drawing Large Graphs with a Potential-Field-Based Multilevel Algorithm (https://www.notion.so/Drawing-Large-Graphs-with-a-Potential-Field-Based-Multilevel-Algorithm-27a6b266bb2c4c92976ed04d2afe9bed?pvs=21), ACE: A Fast Multiscale Eigenvectors Computation for Drawing Huge Graphs (https://www.notion.so/ACE-A-Fast-Multiscale-Eigenvectors-Computation-for-Drawing-Huge-Graphs-44f8183954f14ada944d642d9ff1298e?pvs=21), Drawing Big Graphs Using Spectral Sparsification (https://www.notion.so/Drawing-Big-Graphs-Using-Spectral-Sparsification-89da2043d0444f75a6c663b1fc999512?pvs=21), Multi-Level Graph Layout on the GPU (https://www.notion.so/Multi-Level-Graph-Layout-on-the-GPU-916f610d6b534e48a2478e5910bf085e?pvs=21), Drawing Large Graphs with a Potential-Field-Based Multilevel Algorithm (https://www.notion.so/Drawing-Large-Graphs-with-a-Potential-Field-Based-Multilevel-Algorithm-a4fe70c68ad64ed3849c47d95afc8798?pvs=21), SDE: Graph Drawing Using Spectral Distance Embedding (https://www.notion.so/SDE-Graph-Drawing-Using-Spectral-Distance-Embedding-a840905de8674a85a99706d2c17505aa?pvs=21), Multi-level Graph Drawing Using Infomap Clustering (https://www.notion.so/Multi-level-Graph-Drawing-Using-Infomap-Clustering-ad97d205cc804c748ab87d3012b18ebc?pvs=21), Online Dynamic Graph Drawing (https://www.notion.so/Online-Dynamic-Graph-Drawing-ae22e4cc10ec451bb95c2ba6cfc35499?pvs=21), Eigensolver methods for progressive multidimensional scaling of large data (https://www.notion.so/Eigensolver-methods-for-progressive-multidimensional-scaling-of-large-data-b5ab815edb48469192605e5ad31329c0?pvs=21), An Experimental Comparison of Fast Algorithms for Drawing General Large Graphs (https://www.notion.so/An-Experimental-Comparison-of-Fast-Algorithms-for-Drawing-General-Large-Graphs-bbb7bb7d51d84d109030dee3c06d895d?pvs=21), A Multilevel Algorithm for Force-Directed Graph Drawing (https://www.notion.so/A-Multilevel-Algorithm-for-Force-Directed-Graph-Drawing-d1ebfc7319b04b66833d7e71c060d400?pvs=21), Large-Graph Layout with the Fast Multipole Multilevel Method (https://www.notion.so/Large-Graph-Layout-with-the-Fast-Multipole-Multilevel-Method-def1fdc467f441abb94868eccd8a5a34?pvs=21), Topological fisheye views for visualizing large graphs (https://www.notion.so/Topological-fisheye-views-for-visualizing-large-graphs-e2f5c279f5514a6da3a0241c978f564d?pvs=21) cleaned format?: No duplicate?: No link works?: No Added in paper: No Origin paper plaintext: A Combined Evolutionary Search and Multilevel Optimisation Approach to Graph Partitioning, Mesh Partitioning: a Multilevel Balancing and Refinement Algorithm Page id: e40b37a1147942d89ff1d8dfad285256 unavailable/skip: Yes Cleaned ALL data: No first look: No Related to Literature - Algorithm (Dataset tag relations) 1: A Multilevel Algorithm for Force-Directed Graph Drawing (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/A%20Multilevel%20Algorithm%20for%20Force-Directed%20Graph%20Dr%20234ec1ad86724e0baed5830877d0c70d.md), Multi-Level Graph Layout on the GPU (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Multi-Level%20Graph%20Layout%20on%20the%20GPU%20f855194c9d8a40b8821fafe30add0699.md), ACE: A Fast Multiscale Eigenvectors Computation for Drawing Huge Graphs (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/ACE%20A%20Fast%20Multiscale%20Eigenvectors%20Computation%20for%205e8de72ea8d0436babf760ca379cc457.md), Drawing graphs by eigenvectors: theory and practice (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Drawing%20graphs%20by%20eigenvectors%20theory%20and%20practice%2039ccd4aeec6448f8a58d51b986c9d097.md), SDE: Graph Drawing Using Spectral Distance Embedding (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/SDE%20Graph%20Drawing%20Using%20Spectral%20Distance%20Embeddin%208ce58ef9bc5d4e1aa66f2935bf122f92.md), Efficient and High Quality Force-Directed Graph Drawing (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Efficient%20and%20High%20Quality%20Force-Directed%20Graph%20Dr%201e92a86c6b4f4577b1c2c30903173220.md), Large-Graph Layout with the Fast Multipole Multilevel Method (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Large-Graph%20Layout%20with%20the%20Fast%20Multipole%20Multile%20b88c56b7799741ccbbb9d4f05ea8df4b.md), Drawing Large Graphs with a Potential-Field-Based Multilevel Algorithm (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Drawing%20Large%20Graphs%20with%20a%20Potential-Field-Based%20%203c0831d7e44545b0894bb5b8a4aa8f54.md), Drawing Big Graphs Using Spectral Sparsification (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Drawing%20Big%20Graphs%20Using%20Spectral%20Sparsification%20e5d3efdca48541f2b1789ec74357ebf6.md), An Experimental Comparison of Fast Algorithms for Drawing General Large Graphs (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/An%20Experimental%20Comparison%20of%20Fast%20Algorithms%20for%20%20190e5036cf974a879b50614cfff525f1.md), Eigensolver methods for progressive multidimensional scaling of large data (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Eigensolver%20methods%20for%20progressive%20multidimension%20b4b33cff0cf94e8db1fd3dcdab73f9e4.md), Topological fisheye views for visualizing large graphs (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Topological%20fisheye%20views%20for%20visualizing%20large%20gr%2094f9c1efaeed4304bd4c6686d2962159.md), Online Dynamic Graph Drawing (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Online%20Dynamic%20Graph%20Drawing%203c5e54c02d0b473294442f7387ddb03d.md), Multi-level Graph Drawing Using Infomap Clustering (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Multi-level%20Graph%20Drawing%20Using%20Infomap%20Clustering%20eadf3605c6ed46ff93629fb92858fbd4.md), A Random Sampling O(n) Force-calculation Algorithm for Graph Layouts (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/A%20Random%20Sampling%20O(n)%20Force-calculation%20Algorithm%2086599a831f314d1cb8871a5a92420d0f.md), Sublinear Time Force Computation for Big Complex Network Visualization (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Sublinear%20Time%20Force%20Computation%20for%20Big%20Complex%20N%20999af7d2d1c942628bada6371a87100f.md)
2 Body
Descriptions from Literature
From “An Experimental Comparison of Fast Algorithms for Drawing General Large Graphs”:
The rest of the test graphs are taken from real-world applications. In particular, we selected graphs from the AT&T graph library [1], from C. Walshaw’s graph collection [20], and a graph that describes a social network of 2113 people that we obtained from C. Lipp.
From “A Combined Evolutionary Search and Multilevel Optimisation Approach to Graph-Partitioning”:
The test graphs have been chosen to be a representative sample of small to medium scale real-life problems (mostly mesh-based) and include both 2D and 3D examples of nodal graphs (where the mesh nodes are partitioned) and dual graphs (where the mesh elements are partitioned). In addition there is a 3D semi-structured graph, cti, which is unstructured in the x−y plane but extended regularly along the z-axis. Finally the test suite includes three non mesh-based graphs (add32, vibrobox, bcsstk32) which arise from various scientific computing applications2. None of the graphs have either vertex or edge weights; such graphs are not widely available since most applications do not accurately instrument costs and it is difficult to draw meaningful conclusions from the few examples that we have access to.
Example Figures
From “An Experimental Comparison of Fast Algorithms for Drawing General Large Graphs”:
From “Multi-Level Graph Layout on the GPU”:
Fig. 11. 4elt. Red: our layout, black: Kamada-Kawai layout
Other
From Group DIMACS10 in the SuiteSparse Matrix Collection:
Chris Walshaw's graph partitioning archive contains 34 graphs that
have been very popular as benchmarks for graph partitioning algorithms
( http://staffweb.cms.gre.ac.uk/~wc06/partition/ ).
17 of them are already in the UF Collection. Only the 17 new graphs
not yet in the collection are added here in the DIMACS10 set.
DIMACS10 graph: new? UF matrix:
--------------- ---- -------------
walshaw/144 * DIMACS10/144
walshaw/3elt AG-Monien/3elt
walshaw/4elt Pothen/barth5
walshaw/598a * DIMACS10/598a
walshaw/add20 Hamm/add20
walshaw/add32 Hamm/add32
walshaw/auto * DIMACS10/auto
walshaw/bcsstk29 HB/bcsstk29
walshaw/bcsstk30 HB/bcsstk30
walshaw/bcsstk31 HB/bcsstk31
walshaw/bcsstk32 HB/bcsstk32
walshaw/bcsstk33 HB/bcsstk33
walshaw/brack2 AG-Monien/brack2
walshaw/crack AG-Monient/crack
walshaw/cs4 * DIMACS10/cs4
walshaw/cti * DIMACS10/cti
walshaw/data * DIMACS10/data
walshaw/fe_4elt2 * DIMACS10/fe_4elt2
walshaw/fe_body * DIMACS10/fe_body
walshaw/fe_ocean * DIMACS10/fe_ocean
walshaw/fe_pwt Pothen/pwt
walshaw/fe_rotor * DIMACS10/fe_rotor
walshaw/fe_sphere * DIMACS10/fe_sphere
walshaw/fe_tooth * DIMACS10/fe_tooth
walshaw/finan512 Mulvey/finan512
walshaw/m14b * DIMACS10/m14b
walshaw/memplus Hamm/memplus
walshaw/t60k * DIMACS10/t60k
walshaw/uk * DIMACS10/uk
walshaw/vibrobox Cote/vibrobox
walshaw/wave AG-Monien/wave
walshaw/whitaker3 AG-Monien/whitaker3
walshaw/wing * DIMACS10/wing
walshaw/wing_nodal * DIMACS10/wing_nodal=== STOP RENDERING ===
Made explicit that they want people to reach out to them for the dataset so we do not collect it. Also in Suite Sparse, and many of the graphs are included in the Scotch graph collection.
From: An Experimental Comparison of Fast Algorithms for Drawing General Large Graphs
Graphs found on the Sparse Matrix Collection