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6 results for “pagerank”
Pagerank Dataset for Bitcoin Blockchain - Part 2 of 2
<p><strong>Description</strong></p> <p>This dataset contains the Pagerank values and rankings of Bitcoin addresses and transaction IDs (TXID). It contains a total of 1.608.748.675 addresses or TXIDs.</p> <p>Part 1 is available at <a href="https://zenodo.org/record/6052811">https://zenodo.org/record/6052811</a></p> <p> </p> <p><strong>File format</strong></p> <p>The dataset is compressed with bzip2. It can be uncompressed using the command bunzip2. The dataset is divided into multiple files since it was large. The files are space-delimited plain text files and have the following five fields:</p> <p><Label> <Label type> <Rank> <Rank with ties> <Pagerank value></p> <p>Label: A alphanumeric Bitcoin address (e.g. 1DzTCMmWABEDM1rYFL1RgdLyE59jXMzEHV) or a 64 character hexadecimal transaction ID (e.g. 000000000fdf0c619cd8e0d512c7e2c0da5a5808e60f12f1e0d01522d2986a51) Type: String</p> <p>Label type: It's value is 0 if the label is transaction ID and 1 if the label is a Bitcoin address. Type: Integer</p> <p>Rank: Unique Pagerank rank where the ties (addresses having the same Pagerank value) are resolved by sorting the addresses. Type: Integer</p> <p>Rank with ties: Pagerank rank where the ties (addresses having the same Pagerank value) have the same rank. Type: Integer</p> <p>Pagerank value: Pagerank of the address and transaction IDs calculated using Pagerank algorithm. Type: Floating-point number</p> <p> </p> <p>Sample lines:</p> <p>000000000fdf0c619cd8e0d512c7e2c0da5a5808e60f12f1e0d01522d2986a51 0 427225664 266976712 0.979246<br> 1DzTCMmWABEDM1rYFL1RgdLyE59jXMzEHV 1 1114666798 508037940 0.877961</p> <p> </p> <p><strong>Dataset Generation</strong></p> <p>The Bitcoin transactions between blocks 0 (mined on 03.01.2009) and 713.999 (mined on 13.12.2021) are extracted. A transaction graph is constructed, where Bitcoin addresses and transaction IDs are nodes of the graph and the transaction inputs and outputs are edges of the graph. Pagerank is applied on this transaction graph. This computation is performed using the system presented in the paper 'Parallel analysis of Ethereum blockchain transaction data using cluster computing'.</p> <p> </p> <p><strong>Note</strong></p> <p>If you use our dataset in your research, please cite our paper: <a href="https://link.springer.com/article/10.1007/s10586-021-03511-0">https://link.springer.com/article/10.1007/s10586-021-03511-0</a></p> <pre><code>@article{kilic2022parallel, title={Parallel Analysis of Ethereum Blockchain Transaction Data using Cluster Computing}, journal={Cluster Computing}, author={K{\i}l{\i}{\c{c}}, Baran and {\"O}zturan, Can and Sen, Alper}, year={2022}, month={Jan} }</code></pre> <p> </p> <p><strong>Other Datasets</strong></p> <p>If you are interested, please also check out our <a href="https://zenodo.org/record/6038419">Pagerank Dataset for Ethereum Blockchain</a>.</p> <p> </p>
Pagerank Dataset for Bitcoin Blockchain - Part 1 of 2
<p><strong>Description</strong></p> <p>This dataset contains the Pagerank values and rankings of Bitcoin addresses and transaction IDs (TXID). It contains a total of 1.608.748.675 addresses or TXIDs.</p> <p>Part 2 is available at <a href="https://zenodo.org/deposit/6077428">https://zenodo.org/deposit/6077428</a></p> <p> </p> <p><strong>File format</strong></p> <p>The dataset is compressed with bzip2. It can be uncompressed using the command bunzip2. The dataset is divided into multiple files since it was large. The files are space-delimited plain text files and have the following five fields:</p> <p><Label> <Label type> <Rank> <Rank with ties> <Pagerank value></p> <p>Label: A alphanumeric Bitcoin address (e.g. 1DzTCMmWABEDM1rYFL1RgdLyE59jXMzEHV) or a 64 character hexadecimal transaction ID (e.g. 000000000fdf0c619cd8e0d512c7e2c0da5a5808e60f12f1e0d01522d2986a51) Type: String</p> <p>Label type: It's value is 0 if the label is transaction ID and 1 if the label is a Bitcoin address. Type: Integer</p> <p>Rank: Unique Pagerank rank where the ties (addresses having the same Pagerank value) are resolved by sorting the addresses. Type: Integer</p> <p>Rank with ties: Pagerank rank where the ties (addresses having the same Pagerank value) have the same rank. Type: Integer</p> <p>Pagerank value: Pagerank of the address and transaction IDs calculated using Pagerank algorithm. Type: Floating-point number</p> <p> </p> <p>Sample lines:</p> <p>000000000fdf0c619cd8e0d512c7e2c0da5a5808e60f12f1e0d01522d2986a51 0 427225664 266976712 0.979246<br> 1DzTCMmWABEDM1rYFL1RgdLyE59jXMzEHV 1 1114666798 508037940 0.877961</p> <p> </p> <p>"head.txt" contains the first 10 lines of each file. "tail.txt" contains the last 10 lines of each file.</p> <p> </p> <p><strong>Dataset Generation</strong></p> <p>The Bitcoin transactions between blocks 0 (mined on 03.01.2009) and 713.999 (mined on 13.12.2021) are extracted. A transaction graph is constructed, where Bitcoin addresses and transaction IDs are nodes of the graph and the transaction inputs and outputs are edges of the graph. Pagerank is applied on this transaction graph. This computation is performed using the system presented in the paper 'Parallel analysis of Ethereum blockchain transaction data using cluster computing'.</p> <p> </p> <p><strong>Note</strong></p> <p>If you use our dataset in your research, please cite our paper: <a href="https://link.springer.com/article/10.1007/s10586-021-03511-0">https://link.springer.com/article/10.1007/s10586-021-03511-0</a></p> <pre><code>@article{kilic2022parallel, title={Parallel Analysis of Ethereum Blockchain Transaction Data using Cluster Computing}, journal={Cluster Computing}, author={K{\i}l{\i}{\c{c}}, Baran and {\"O}zturan, Can and Sen, Alper}, year={2022}, month={Jan} }</code></pre> <p> </p> <p><strong>Other Datasets</strong></p> <p>If you are interested, please also check out our <a href="https://zenodo.org/record/6038419">Pagerank Dataset for Ethereum Blockchain</a>.</p> <p> </p>
Pagerank Dataset for Ethereum Blockchain
<p><strong>Description</strong></p> <p>This dataset contains the Pagerank values and rankings of 147.098.561 Ethereum addresses.</p> <p> </p> <p><strong>File format</strong></p> <p>The dataset is compressed with bzip2. It can be uncompressed using the command bunzip2. It is a space-delimited plain text file and has the following four fields:</p> <p><Ethereum Address> <rank> <rank with ties> <Pagerank value></p> <p>Ethereum Address: A 42-character hexadecimal Ethereum address in the lowercase form (not in checksummed (mixed-case) form). E.g. 0x3f5ce5fbfe3e9af3971dd833d26ba9b5c936f0be</p> <p>rank: Unique Pagerank rank where the ties (addresses having the same Pagerank value) are resolved by sorting the addresses by hexadecimal value</p> <p>rank with ties: Pagerank rank where the ties (addresses having the same Pagerank value) have the same rank.</p> <p>Pagerank value: Pagerank of the address calculated using Pagerank algorithm.</p> <p> </p> <p><strong>Dataset Generation</strong></p> <p>The Ethereum transactions between blocks 0 (mined on 30.07.2015) and 13.799.999 (mined on 14.12.2021) are extracted. A transaction graph is constructed, where Ethereum addresses are nodes of the graph and the transactions are edges of the graph. Pagerank is applied on this transaction graph. This computation is performed using the system presented in the paper 'Parallel analysis of Ethereum blockchain transaction data using cluster computing'.</p> <p> </p> <p><strong>Note</strong></p> <p>If you use our dataset in your research, please cite our paper: <a href="https://link.springer.com/article/10.1007/s10586-021-03511-0">https://link.springer.com/article/10.1007/s10586-021-03511-0</a></p> <pre><code>@article{kilic2022parallel, title={Parallel Analysis of Ethereum Blockchain Transaction Data using Cluster Computing}, journal={Cluster Computing}, author={K{\i}l{\i}{\c{c}}, Baran and {\"O}zturan, Can and Sen, Alper}, year={2022}, month={Jan} }</code></pre> <p> </p> <p><strong>Other Datasets</strong></p> <p>If you are interested, please also check out our <a href="https://zenodo.org/record/6052811">Pagerank Dataset for Bitcoin Blockchain</a>.</p>
Temporal Patterns and Trends in Corporate Donations Using PageRank and Node Similarity Graph Algorithm
<p>Corporate donations wield considerable influence within political arenas, shaping policies and influencing decision-making processes. This study uses Neo4j, an advanced graph database tool, to explore a comprehensive company dataset, focusing on unraveling temporal patterns and evolving trends in corporate contributions. Visual representations, such as bar charts, reveal significant fluctuations in donations, indicating potential cyclic patterns occurring every six years. The study explores intricate relationships between donor entities and recipients, highlighting diverse donation patterns—both focused and widespread. The study's derived PageRank scores offer a comprehensive portrayal of the varying degrees of influence among diverse entities receiving donations within the network. Notably, the Conservative and Unionist Party emerges as the most prominent entity, boasting a striking score of 1.86, indicating a substantial influx of financial support likely to significantly shape its political endeavors. Despite a lower score of 0.62, the Labor Party still signifies a noteworthy level of financial backing, albeit less extensive than its counterpart. In contrast, the Liberal Democrats, The In Campaign Ltd, and Network for Animals Ltd exhibit comparatively restrained financial backing, warranting deeper investigation into the factors affecting their funding. Moreover, undisclosed findings regarding 170 similarity scores using Node Similarity algorithm disclose a prevalent similarity trend among entities, notably observed between Company 1 and Company 2, implying potential synergistic partnerships in donation-related endeavors. This high similarity often indicates shared values, highlighting prospects for collaborative initiatives or partnerships to augment positive impacts. Utilizing these insights supports the formulation of targeted donation strategies, circumventing donation redundancies, and ensuring optimal resource allocation for maximal societal benefit within specified sectors.</p> <p>Keywords—Company Dataset, Corporate Donations, Neo4j, Node Similarity, PageRank, Political Influence </p> <p> </p>
Results from "The Field-Dependent Nature of PageRank Values in Citation Networks"
<p>This repo contains a gzipped archive of the resulting dataframes from the analyses discussed in our manuscript</p>
Knowledge Graph Example, calculated PageRank and HITS
<p>The file contains the graph as json.</p> <p>Where do I add a repository? ;)</p> <p><a href="https://github.com/AntonioNoack/WebPageRank/commit/0be63ef218f32676ef74b1077e375470233f2b9b">https://github.com/AntonioNoack/WebPageRank/commit/0be63ef218f32676ef74b1077e375470233f2b9b</a> was my last commit, when I created this file. The project there was used to calculate the PageRank and HITS values.</p> <p> </p> <p>Normalization: None<br> PageRank random jump probability: 15%<br> PageRank preference vector: None</p>
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