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14 results for “Temporal Graph”
Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
<p>Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"<br> URL: https://github.com/qianghuangwhu/benchtemp</p> <p>Openreview: https://openreview.net/forum?id=rnZm2vQq31</p> <p><br> There are 19 (15+4) benchmark temporal graph datasets:<br> reddit,<br> wikipedia,<br> mooc,<br> lastfm,<br> enron,<br> SocialEvo,<br> uci,<br> CollegeMsg,<br> TaobaoSmall,<br> CanParl,<br> Contacts,<br> Flights,<br> UNtrade,<br> USLegis,<br> UNvote,</p> <p>DGraphFin,</p> <p>TaobaoLarge,</p> <p>YoutubeReddit,</p> <p>YoutubeRedditLarge</p> <p> </p> <p><br> Each dataset has three files:<br> 1. ml_{data_name}.csv - the csv file of the Temporal Graph.</p> <p>This file have five columns with properties:</p> <p>'u': the id of the user.<br> 'i': the id of the item.<br> 'ts': the timestamp of the interaction (edge) between the user and the item.<br> 'label': the label of the interaction (edge).<br> 'idx': the index of the interaction (edge).<br> For example:</p> <p>,u,i,ts,label,idx<br> 0,1,2,0.0,0.0,1<br> 1,1,3,0.0,0.0,2<br> 2,1,4,0.0,0.0,3<br> 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph..</p> <p>3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.</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>
PubMed-Temporal: A dynamic graph dataset with node-level features
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ORBITAAL: cOmpRehensive BItcoin daTaset for temporAl grAph anaLysis
<h3>Dataset Construction</h3> <p>This dataset captures the temporal network of Bitcoin (BTC) flow exchanged between entities at the finest time resolution in UNIX timestamp. Its construction is based on the blockchain covering the period from January, 3rd of 2009 to January the 25th of 2021. The blockchain extraction has been made using bitcoin-etl (<a href="https://github.com/blockchain-etl/bitcoin-etl">https://github.com/blockchain-etl/bitcoin-etl</a>) Python package. The entity-entity network is built by aggregating Bitcoin addresses using the common-input heuristic [1] as well as popular Bitcoin users' addresses provided by <a href="https://www.walletexplorer.com/">https://www.walletexplorer.com/</a></p> <p>[1] M. Harrigan and C. Fretter, "The Unreasonable Effectiveness of Address Clustering," <em>2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)</em>, Toulouse, France, 2016, pp. 368-373, doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0071.<br>keywords: {Online banking;Merging;Protocols;Upper bound;Bipartite graph;Electronic mail;Size measurement;bitcoin;cryptocurrency;blockchain},</p> <p> </p> <h3>Dataset Description</h3> <p><strong>Bitcoin Activity Temporal Coverage</strong>: From 03 January 2009 to 25 January 2021</p> <h4>Overview:</h4> <p>This <strong>dataset </strong>provides a <strong>comprehensive</strong> representation of <strong>Bitcoin exchanges</strong> between entities over a s<strong>ignificant temporal span</strong>, spanning from the inception of Bitcoin to recent years. It encompasses <strong>various temporal resolutions</strong> and <strong>representations</strong> to <strong>facilitate Bitcoin transaction network analysis </strong>in the context of <strong>temporal graphs</strong>.</p> <p>Every dates have been retrieved from bloc UNIX timestamp and GMT timezone.</p> <h4>Contents:</h4> <p>The dataset is distributed across three compressed archives:</p> <p>All data are stored in the <strong>Apache Parquet file format</strong>, a columnar storage format optimized for analytical queries. It can be used with pyspark Python package.</p> <ol> <li> <p><strong>orbitaal-stream_graph.tar.gz</strong>:</p> <ul> <li>The root directory is <em>STREAM_GRAPH/</em></li> <li>Contains a <strong>stream graph</strong> representation of Bitcoin exchanges at the <strong>finest temporal scale</strong>, corresponding to the validation time of <strong>each block</strong> (averaging approximately 10 minutes).</li> <li>The stream graph is divided into 13 files, one for each year</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-stream_graph-date-[YYYY]-file-id-[ID].snappy.parquet,</strong> where <em>[YYYY]</em> stands for the corresponding <em>year</em> and <em>[ID]</em> is <em>an integer</em> from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering</li> <li>These files are in the subdirectory <em>STREAM_GRAPH/EDGES/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-all.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>snapshot</strong> network representing <strong>all transactions aggregated </strong>over the whole dataset period (from Jan. 2009 to Jan. 2021).</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-all.snappy.parquet</strong>.</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/ALL/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-year.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>yearly</strong><em> </em>resolution of <strong>snapshot</strong> networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-file-id-[ID].snappy.parquet</strong>, where <em>[YYYY]</em> stands for the corresponding <em>year </em>and <em>[ID]</em> is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/year/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-month.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>monthly </strong>resoluted <strong>snapshot </strong>networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-[MM]-file-id-[ID].snappy.parquet</strong>, where</li> <li><em>[YYYY] </em>and <em>[MM] </em>stands for the corresponding <em>year </em>and <em>month, </em>and <em>[ID] </em>is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year and month ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/month/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-day.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>daily </strong>resoluted <strong>snapshot </strong>networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-file-id-[ID].snappy.parquet</strong>, where</li> <li><em>[YYYY]</em>, <em>[MM]</em>, and <em>[DD] </em>stand for the corresponding <em>year</em>, <em>month</em>, and <em>day</em>, and <em>[ID] </em>is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, and day ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/day/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-hour.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>hourly </strong>resoluted <strong>snapshot </strong>networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-[hh]-file-id-[ID].snappy.parquet</strong>, where</li> <li><em>[YYYY]</em>, <em>[MM]</em>, <em>[DD]</em>, and <em>[hh]</em> stand for the corresponding <em>year, month, day, </em>and <em>hour</em>, and <em>[ID] </em>is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, day and hour ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/hour/</em></li> </ul> </li> <li> <p><strong>orbitaal-nodetable.tar.gz</strong>:</p> <ul> <li>The root directory is <em>NODE_TABLE/</em></li> <li>Contains two files in parquet format, the first one gives <strong>information </strong>related to <strong>nodes </strong>present in stream graphs and snapshots such as <strong>period of activity</strong> and associated global <strong>Bitcoin balance</strong>, and the other one contains the list of <strong>all associated Bitcoin addresses.</strong></li> </ul> </li> </ol> <p> </p> <p>Small samples in CSV format</p> <ol> <li> <p><strong>orbitaal-stream_graph-2016_07_08.csv</strong> and <strong>orbitaal-stream_graph-2016_07_09.csv</strong></p> <ul> <li>These two CSV files are related to stream graph representations of an halvening happening in 2016.</li> </ul> </li> <li> <p><strong>orbitaal-snapshot-2016_07_08.csv </strong>and<strong> orbitaal-snapshot-2016_07_09.csv</strong></p> <ul> <li>These two CSV files are related to daily snapshot representations of an halvening happening in 2016.</li> <li><strong> </strong></li> </ul> </li> </ol> <p> </p> <p> </p> <p> </p>
Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
<p>Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"<br> URL: https://github.com/qianghuangwhu/benchtemp</p> <p>Openreview: https://openreview.net/forum?id=rnZm2vQq31</p> <p><br> There are 19 (15+4) benchmark temporal graph datasets:<br> reddit,<br> wikipedia,<br> mooc,<br> lastfm,<br> enron,<br> SocialEvo,<br> uci,<br> CollegeMsg,<br> TaobaoSmall,<br> CanParl,<br> Contacts,<br> Flights,<br> UNtrade,<br> USLegis,<br> UNvote,</p> <p>DGraphFin,</p> <p>TaobaoLarge,</p> <p>YoutubeReddit,</p> <p>YoutubeRedditLarge</p> <p> </p> <p><br> Each dataset has three files:<br> 1. ml_{data_name}.csv - the csv file of the Temporal Graph.</p> <p>This file have five columns with properties:</p> <p>'u': the id of the user.<br> 'i': the id of the item.<br> 'ts': the timestamp of the interaction (edge) between the user and the item.<br> 'label': the label of the interaction (edge).<br> 'idx': the index of the interaction (edge).<br> For example:</p> <p>,u,i,ts,label,idx<br> 0,1,2,0.0,0.0,1<br> 1,1,3,0.0,0.0,2<br> 2,1,4,0.0,0.0,3<br> 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph..</p> <p>3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.</p>
Large-scale temporal graph datasets
<p>Large scale datasets used for evaluation in the article: `Optimizing the Interval-centric Distributed<br> Computing Model for Temporal Graph Algorithms` to appear in `EuroSys 2022`.</p>
Towards Neural Scaling Laws for Foundation Models on Temporal Graphs
<p>Datasets provided in this storage are introduced in the paper: <em>Towards Neural Scaling Laws for Foundation Models on Temporal Graphs</em></p> <ul> <li>Each .csv file represents all transactions of the token network that has the same name as the file name (<em><tokenname.csv>)</em></li> <li>Each transaction corresponds to a row in each file.</li> <li>Each transaction has: <ul> <li> blockNumber : is the block ID of Ethereum that includes this transaction</li> <li>timestamp: time that the transaction is made in UNIX timestamp format</li> <li>tokenAddress : the address that specifies a unique ERC20 token</li> <li>from: address of sender</li> <li>to: address of receiver</li> <li>value: the amount the transaction</li> <li>fileBlock: we split the whole number of blocks count to 35 buckets and assigned the bucket ID to the transaction to trace the blocks </li> </ul> </li> <li>To use the same setting as described in the papers, we include edge list and label that contain node interactions and labels for each snapshot in each token network. <ul> <li>Each transaction in the edge list also has "from","to" and "amount" fields, but with an additional "snapshot" field to indicate the index of the snapshot that the transaction below to</li> <li>Each row in label file indicates the ground truth label of the snapshot having an index corresponding to the index of the row (e.g first row indicates the label of the first snapshot)</li> <li>We provided the way to generate edge lists and label files in the following Github repository: https://github.com/benjaminnNgo/ScalingTGNs/blob/main/script/utils/TGS.py</li> </ul> </li> <li>However, we also provide raw <em>.csv </em> to divide into generate <em>edgeslist </em>and <em>label with a different setting.</em></li> </ul> <p><br><br></p>
Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks
<p>Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks</p>
Temporal Event Knowledge Graphs transformed from Object-Centric Event Logs
<p>In the paper"Transforming Object-Centric Event Logs to Temporal Event Knowledge Graphs", we introduced and formalized temporal Event Knowledge Graphs (tEKGs) and presented an algorithm to transform Object-Centric Event Logs (OCEL) 2.0 into tEKGs. Data sets are the results of transforming OCEL 2.0 log files. The source of the generated dump files are as follows:</p> <ol> <li>ContainerLogistics.neo4j.dum : <a href="../records/8428084">Link to the source data</a></li> <li>OrderManagement.neo4j.dump: <a href="../records/8428112">Link to the source data</a></li> <li>Procure-To-Payment.neo4j.dump: <a href="../records/8412920">Link to the source data</a></li> </ol> <p>The version of the dump files is <strong>5.12.0</strong>. Additionally, for restoring dump files inside Neo4j, you need to enter the username and password, which is indicated below:</p> <p><strong>Username</strong>: neo4j</p> <p><strong>Password</strong>: 12345678</p> <p> </p>
Video simulations for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"
<p>Videos of the comparison between numerical and deep learning simulations for test datasets 1, 2, and 3 for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks".</p>
Raw datasets for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"
<p>Raw datasets for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks".</p> <p>The zip folder comprises 4 subfolders (DEM, WD, VX, VY), containing the elevation, water depths in time, and velocities (in x and y directions) in time for all training and testing simulations. The overview.csv file provides the runtime of the numerical model on each different simulation, identified by its id.</p> <p>The simulations ids are divided as follows:</p> <p>- 1-80: Training and validation</p> <p>- 501-520: Testing dataset 1</p> <p>- 10001-10020: Testing dataset 2</p> <p>- 15001-15020: Testing dataset 3</p>
Structure-function coupling increases during interictal spikes in temporal lobe epilepsy: a graph signal processing study
<p><strong>Dataset for the publication: 'Structure-function coupling increases during interictal spikes in temporal lobe epilepsy: a graph signal processing study'<br> Rigoni et al. 2023, Clinical Neurophysiology, doi: </strong><a href="https://doi.org/10.1016/j.clinph.2023.05.012">https://doi.org/10.1016/j.clinph.2023.05.012</a></p> <p><strong>Dataset description</strong></p> <p><em>func_data.mat</em> : source-reconstructed EEG traces stored in a Fieldtrip format (data for each subj)</p> <p><em>struct_data</em>: consensus structural connectome</p> <p><em>ROIpatch </em>: mesh used to plot Fig4</p> <p><em>SC_surrogates (W0)</em>: 1000 degree-preserving surrogates of the structural connectome computed with the null_model_und_sign function of the Brani Connectivity Toolbox; W0 are used for broadcasting analyses, section 2.6.2</p> <p><em>SC_surrogates_harmonics (U0)</em> : network harmonics of the surrogate structural connectomes (W0); U0 are used for broadcasting analyses, section 2.6.2<br> <em>BD_surr </em>: Broadcasting-direction (BD) of the degree-preserving surrogates of the structural connectome W0; used to produce FigS3 (data for each subj)</p> <p><em>PHI </em>: matrix of +1/-1 to generate the functional surrogates used to define significance of SDI, as described in section 2.6.3</p> <p><em>data_GSP2_surr </em>: SDI of the functional surrogates used to define significance of SDI, as described in section 2.6.3 (data for each subj)</p> <p> </p> <p>Abbreviations:</p> <p>EEG= electroencephalography;</p> <p>SDI= structure-decoupling index</p>
S-Cypher: A Temporal Query Language on the Temporal Property Graph Model
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Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
<p>Dataset for ICLR 2021 submission "Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling".</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.