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3 results for “evolving graph”
Lehigh University Benchmark (LUBM): Evolving Graph Simulation
<p>The Lehigh University Benchmark (LUBM) generates benchmark datasets containing people working at universities [1]. We use the Data Generator v1.7 to generate 10 versions of a graph containing 100 universities [2].<br> Thus, all versions are of similar size, but we emulate modifications by generating different vertex identifiers, i.e., each version is considered a timestamped graph. Each graph contains about 2.1 M vertices and 13 M edges.<br> Over all versions, the mean degree is 6.7 (+- 0.1), the mean in-degree is 6.8 (+- 0.1), and the mean out-degree is 5.1 (+- 0.1).</p> <p>1. <a href="https://dblp.uni-trier.de/pid/80/5390.html">Yuanbo Guo</a>, <a href="https://dblp.uni-trier.de/pid/48/6834.html">Zhengxiang Pan</a>, <a href="https://dblp.uni-trier.de/pid/94/1154.html">Jeff Heflin</a>: LUBM: A benchmark for OWL knowledge base systems. <a href="https://dblp.uni-trier.de/db/journals/ws/ws3.html#GuoPH05">J. Web Semant. 3(2-3)</a>: 158-182 (2005)</p> <p>2. <a href="https://dblp.uni-trier.de/pid/222/6353.html">Till Blume</a>, <a href="https://dblp.uni-trier.de/pid/r/DavidRicherby.html">David Richerby</a>, <a href="https://dblp.uni-trier.de/pid/06/2380.html">Ansgar Scherp</a>: Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs. <a href="https://dblp.uni-trier.de/db/conf/cikm/cikm2020.html#BlumeRS20">CIKM 2020</a>: 75-84</p>
Berlin Sparql Benchmark (BSBM): Evolving Graph Simulation
<p>The Berlin SPARQL Benchmark (BSBM) is a suite of benchmarks built around an e-commerce use case [1]. We generated 21 versions of the dataset with different scale factors. The first dataset, with a scale factor of 100, contains about 7,000 vertices and 75,000 edges. We generated versions with scale factors between 2,000 and 40,000 in steps of 2,000. The largest dataset contains about 1.3 M vertices and 13 M edges. For our experiments in [2], we first use the different versions ordered from smallest to largest (version 0 to 20) to simulate a growing graph database. Subsequently, we reverse the order to emulate a shrinking graph database. Over all versions, the mean degree is 8.1 (+- 0.5), the mean in-degree is 4.6 (+- 0.3), and the mean out-degree is 9.8 (+- 0.2).</p> <p>1. <a href="https://dblp.uni-trier.de/pid/b/ChristianBizer.html">Christian Bizer</a>, <a href="https://dblp.uni-trier.de/pid/47/7466.html">Andreas Schultz</a>: The Berlin SPARQL Benchmark. <a href="https://dblp.uni-trier.de/db/journals/ijswis/ijswis5.html#BizerS09">Int. J. Semantic Web Inf. Syst. 5(2)</a>: 1-24 (2009)</p> <p>2. <a href="https://dblp.uni-trier.de/pid/222/6353.html">Till Blume</a>, <a href="https://dblp.uni-trier.de/pid/r/DavidRicherby.html">David Richerby</a>, <a href="https://dblp.uni-trier.de/pid/06/2380.html">Ansgar Scherp</a>: Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs. <a href="https://dblp.uni-trier.de/db/conf/cikm/cikm2020.html#BlumeRS20">CIKM 2020</a>: 75-84</p>
Functionally Important Residues from Graph Analysis of Co-evolved Dynamical Couplings
<p>This dataset contains input files and trajectories for class A β-lactamase SHV-1.</p>
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OpenNeuro
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