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9 results for “Hypergraph”
Large-scale attributed graph & hypergraph datasets: TWeibo, Amazon2M, Amazon, MAG-PM
<p>Here we provide additional large-scale datasets used in our work "A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation", along with the index files for constructing KNN graphs using ScaNN and Faiss.</p> <p>Usage:</p> <p>cd ANCKA/</p> <p>unzip ~/Download_path/ANCKA_data.zip -d data/</p>
Hypergraph Factorisation Expression Quantitative Trait Loci
<p>Please cite:</p> <pre><code>Hypergraph factorisation for multi-tissue gene expression imputation. Vinas Torne, Ramon and Joshi, Chaitanya K. and Georgiev, Dobrik and Lin, Phillip and Dumitrascu, Bianca* and Gamazon, Eric* and Lio, Pietro*. *Co-corresponding authors. </code></pre>
Benchmark Hypergraphs and Detailed Experimental Results of "k-way Hypergraph Partitioning via n-Level Recursive Bisection"
<p>This dataset contains hypergraphs derived from three benchmark sets: The<br /> ISPD98 VLSI Circuit Benchmark Suite [1], the University of Florida Sparse Matrix Collec-<br /> tion [2] and the international SAT Competition 2014 [3]. From the latter, we randomly selected<br /> 100 instances from the application track and converted them into hypergraphs as follows:<br /> Each boolean variable (and its complement) is mapped to one vertex and each clause constitutes<br /> a net [41]. The Sparse Matrix Collection is organized into 172 groups and each group contains<br /> matrices of different application areas. From each group, we choose one matrix for each appli-<br /> cation area that has between 10 000 and 10 000 000 columns. In case multiple matrices fulfill<br /> our criteria, we randomly select one. In total, we include 192 matrices, which are translated into<br /> hypergraphs using the row-net model, i.e. each row is treated as a net and each column as<br /> a vertex. Empty rows are discarded. Both vertices and nets have unit weight. Together with the<br /> 18 ISPD98 VLSI instances , a total of 310 hypergraphs constitute our benchmark set. 4 Each of<br /> these hypergraphs is partitioned into k ∈ {2, 4, 8, 16, 32, 64, 128} blocks with ε = 0.03. For each<br /> value of k, a k-way partition is considered to be one test instance, resulting in a total of 2170<br /> instances.</p> <p>See the README for further information on the different files contained in this dataset.</p> <p>[1 ]C. J. Alpert. The ISPD98 Circuit Benchmark Suite. In Proc. of the 1998 Int. Symp. on Physical Design, ISPD ’98, pages 80–85, New York, 1998. ACM.<br /> [2] T. A. Davis and Y. Hu. The University of Florida Sparse Matrix Collection. ACM Trans. Math. Softw.,38(1):1:1–1:25, 2011.<br /> [3] A. Belov, D. Diepold, M. Heule, and M. Järvisalo. The SAT Competition 2014. http://www.satcompetition.org/2014/, 2014.</p>
A Benchmark Set for Multilevel Hypergraph Partitioning Algorithms
<p>DESCRIPTION<br> -------------------------------------------------------------------------------------------------------<br> This archive contains a large benchmark set for hypergraph partitioning algorithms.<br> All hypergraphs are unweighted (i.e., have unit edge and vertex weights) and use<br> the hMetis hypergraph input file format [1].</p> <p>BENCHMARK SETS<br> -------------------------------------------------------------------------------------------------------<br> Hypergraphs are derived from the following benchmark sets:<br> - The ISPD98 Circuit Benchmark Suite [2]<br> - The DAC 2012 Routability-Driven Placement Contest [3]<br> - The international SAT Competition 2014 [4]<br> - The University of Florida Sparse Matrix Collection (UF-SPM) [5]</p> <p>The benchmark set contains all ISPD98 and DAC2012 instances. Furthermore,<br> it contains 92 randomly selected instances from the application track of the SAT Competition 2014.<br> The Sparse Matrix Collection is organized into 172 groups and each group contains<br> matrices of different application areas. From each group, we chose one matrix for each application <br> area that has between 10 000 and 10.000.000 columns. In case multiple matrices fulfill<br> our criteria, we randomly selected one. In total, we include 192 matrices.</p> <p><br> HYPERGRAPH REPRESENTATION<br> -------------------------------------------------------------------------------------------------------<br> VLSI instances [2,3] are transformed into hypergraphs by converting the netlist into a<br> set of hyperedges. Sparse Matrices are translated into hypergraphs using the row-net model [6],<br> i.e. each row is treated as a net and each column as a vertex. SAT instances are converted into<br> three different hypergraph representations: In the literal model, each boolean literal is mapped to one<br> vertex and each clause constitutes a net [7]. In the primal model each variable is represented by a vertex<br> and each clause is represented by a net, whereas in the dual model the opposite is the case [8].</p> <p>FILE NAMES<br> -------------------------------------------------------------------------------------------------------<br> The origin of each hypergraph (and for SAT instances the hypergraph model) is encoded<br> into the file names as follows:<br> - Sparse Matrices : *.mtx.hgr<br> - DAC2012 : dac2012_superblue*.hgr<br> - ISPD98 : ISPD98_ibm*.hgr<br> - SAT-14 primal : sat14_*.cnf.primal.hgr<br> - SAT-14 dual : sat14_*.cnf.dual.hgr<br> - SAT-14 literal : sat14_*.cnf.hgr</p> <p>REFERENCES<br> -------------------------------------------------------------------------------------------------------<br> [1] http://glaros.dtc.umn.edu/gkhome/fetch/sw/hmetis/manual.pdf<br> [2] C. J. Alpert. The ISPD98 Circuit Benchmark Suite. In Proc. of the 1998 Int. Symp. on Physical Design, pages 80–85, New York, 1998. ACM.<br> [3] N. Viswanathan, C. Alpert, C. Sze, Z. Li, and Y/ Wei. The dac 2012 routability-driven placement contest and benchmark suite. In Proceedings of the 49th Annual Design Automation Conference, DAC ’12, pages 774–782<br> [4] A. Belov, D. Diepold, M. Heule, and M. Järvisalo. The SAT Competition 2014. http://www.satcompetition.org/2014/, 2014.<br> [5] T. A. Davis and Y. Hu. The University of Florida Sparse Matrix Collection. ACM Trans. Math. Softw.,38(1):1:1–1:25, 2011.<br> [6] Ü. V. Catalyürek and C. Aykanat. Hypergraph-partitioning-based decomposition for parallel sparse-matrix vector multiplication. IEEE Transactions on Parallel and Distributed Systems, 10(7):673–693, Jul 1999.<br> [7] D. A. Papa and I. L. Markov. Hypergraph Partitioning and Clustering. In T. F. Gonzalez, editor, Handbook of Approximation Algorithms and Metaheuristics. Chapman and Hall/CRC, 2007.<br> [8] Zoltan Mann and Pal Papp. Formula partitioning revisited. In Daniel Le Berre, editor, POS-14. Fifth Pragmatics of SAT workshop, volume 27 of EPiC Series in Computing, pages 41–56. EasyChair, 2014.</p>
hypergraphs created from meshes of the RPI Formula Hybrid suspension upright
<p>MDS/PUMI meshes were converted to hypergraphs<br> (mesh elements -> graph vertices, mesh vertices -> hyperedges)<br> using the `testFileIO` tool from EnGPar (git hash 82fbd65).<br> </p>
A Benchmark Collection of Hypergraphs
<p>This benchmark currently contains 2191 hypergraph instances that originate from CQs and CSPs instances from various sources. All hypergraphs have been generated and published by W. Fischl, G. Gottlob, D. M. Longo, and R. Pichler (2017) at <a href="http://hyperbench.dbai.tuwien.ac.at">http://hyperbench.dbai.tuwien.ac.at</a> together with different hypergraph properties including various notions of width.</p> <p>See Johannes K. Fichte, Markus Hecher, Neha Lodha, and Stefan Szeider: An SMT Approach to Fractional Hypertree Width, Proceedings of the 24th International Conference on Principles and Practice of Constraint Programming (CP2018) for details on the original sources of the benchmarks.</p>
Cluster configurations of a generalized Deffuant model on hypergraph ensembles
<p>## Data</p> <p>For each measured combination of the confidence and system size, there is one gzipped<br> file. For different ensembles, we collected data in different ranges and quality.<br> The paramters are:</p> <p>* Number of samples `m` per parameter combination<br> * Range `r` of confidences epsilon<br> * Distances `d` between values of epsilon (basically the resolution of the data)<br> * Largest size `N_max`</p> <p>The single files follow a naming scheme of `n{N}_e{epsilon}.cluster.dat.gz`, where<br> `{N}` signals the system size of the simulation and `{epsilon}` is the confidence<br> value of the simulation (without a decimal point, i.e., `0050` corresponds to `epsilon = 0.050`).<br> The sizes `N` are usually powers of two (or for the lattices, perfect squares close to powers of two).</p> <p>We present the data for each ensemble in one folder (after unpacking the tar archive).<br> Note that some parameter values are missing, if they did not converge in reasonable time.</p> <p><br> * Barabasi Albert with a mean degree of `c=9` and hyperedge size of `k=3`: `ba_c9_k3`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Barabasi Albert with a mean degree of `c=10` and hyperedge size of `k=5`: `ba_c10_k5`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=10` hyperedge size `k=3`: `er_c10_k3`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=10` hyperedge size `k=4`: `er_c10_k4`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=10` hyperedge size `k=5`: `er_c10_k5`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=10` hyperedge size `k=6`: `er_c10_k6`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=150` hyperedge size `k=6`: `er_c150_k6`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 16384`<br> * Erdos-Renyi with a mean degree of `c_3=5` and `c_5=5`: `er_c3_5_c5_5`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 16384`<br> * Erdos-Renyi with a mean degree of `c_3=30/8` and `c_5=50/8`: `er_c3_375_c5_625`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 16384`<br> * Lattice a mean degree of `c=12` hyperedge size `k=3`: `lat_c12_k3`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 32761`<br> * Lattice a mean degree of `c=15` hyperedge size `k=5`: `lat_c15_k5`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 16384`</p> <p>## Data format</p> <p>Each final state is encoded as three lines:</p> <p>* The convergence time is a single integer with a line prefix '# sweeps: '<br> * The positions of all clusters in opinion space with a line prefix '# ' (unsorted)<br> * The number of agents in each of the clusters without a line prefix</p> <p><br> ## Python example for reading the format</p> <p>An example script, which visualizes the S vs eps graph for the largest size of the `er_c10_k3`<br> case, with a function to read this format is given in `example.py`.</p>
Large-scale attributed hypergraph datasets: Amazon & MAG-PM
<p>Amazon and MAG-PM are two large-scale datasets of real-world attributed hypergraphs. The ScaNN indices for fast K-nearest neighbor searching are also included.</p><p>For further details, please refer to our publication "Efficient and Effective Attributed Hypergraph Clustering via K-Nearest Neighbor Augmentation" on SIGMOD 2023, and the <a href="https://github.com/CyanideCentral/AHCKA">GitHub repository</a>.</p>
Datasets - Core-periphery Models for Hypergraphs
<p>Contains data for "Core-periphery Models for Hypergraphs" to be presented at KDD 2022.</p>
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OpenNeuro
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