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37 results for “graph analysis”
Dataset and Source Code for the Paper: A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms
<p>Here are the data set and source code related to the paper: "A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms"</p> <p>1- aptnotes-downloader.zip : contains source code that downloads all APT reports listed in https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>2- apt-groups.zip : contains all APT group names gathered from https://docs.google.com/spreadsheets/d/1H9_xaxQHpWaa4O_Son4Gx0YOIzlcBWMsdvePFX68EKU/edit?gid=1864660085#gid=1864660085 and https://malpedia.caad.fkie.fraunhofer.de/actors and https://malpedia.caad.fkie.fraunhofer.de/actors</p> <p>3- apt-reports.zip : contains all deduplicated APT reports gathered from https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>4- countries.zip : contains country name list.</p> <p>5- ttps.zip : contains all MITRE techniques gathered from https://attack.mitre.org/resources/attack-data-and-tools/</p> <p>6- malware-families.zip : contains all malware family names gathered from https://malpedia.caad.fkie.fraunhofer.de/families</p> <p>7- ioc-searcher-app.zip : contains source code that extracts IoCs from APT reports. Extracted IoC files are provided in report-analyser.zip. Original code repo can be found at https://github.com/malicialab/iocsearcher</p> <p>8- extracted-iocs.zip : contains extracted IoCs by ioc-searcher-app.zip</p> <p>9- report-analyser.zip : contains source code that searchs APT reports, malware families, countries and TTPs. I case of a match, it updates files in extracted-iocs.zip.</p> <p>10- cti-transformation-app.zip : contains source code that transforms files in extracted-iocs.zip to CTI triples and saves into Neo4j graph database.</p> <p>11- graph-db-backup.zip : contains volume folder of Neo4j Docker container. When it is mounted to a Docker container, all CTI database becomes reachable from Neo4j web interface. Here is how to run a Neo4j Docker container that mounts folder in the zip:</p> <p>docker run -d --publish=7474:7474 --publish=7687:7687 --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/data:/data --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/plugins:/plugins --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/logs:/logs --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/conf:/conf --env 'NEO4J_PLUGINS=["apoc","graph-data-science"]' --env NEO4J_apoc_export_file_enabled=true --env NEO4J_apoc_import_file_enabled=true --env NEO4J_apoc_import_file_use__neo4j__config=true --env=NEO4J_AUTH=none neo4j:5.13.0</p> <h4><strong>web interface: http://localhost:7474</strong></h4> <h4><strong>username: neo4j</strong></h4> <h4><strong>password: neo4j</strong></h4> <p> </p>
Data Set for the Journal Article "Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates"
<p>This repository containes the data generated by ASAP and discussed in the journal article [Csizi, K.-S. and Reiher, M., 2023, arXiv:2307.16344], including Cartesian coordinates of training and test set molecules, and MD trajectories. </p>
Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis — exact and related matches
<p>Results of the 'Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis' papers. There are 2 datasets:</p> <ul> <li>'exact_matches.csv': contains detailed information about the concepts present both in OpenAlex and OpenAIRE.</li> <li>'related_matches.csv': contains detailed information about the concepts from OpenAlex and OpenAIRE that were not present in both KGs but got aligned following the algorithm presented in the paper.</li> </ul> <p>The detailed information refers to the following column:</p> <ul> <li>Category1: name of the first category</li> <li>Source1: source of the first category ('OpenAlex' or 'OpenAIRE')</li> <li>Category2: name of the second category</li> <li>Source2: source of the first category ('OpenAlex' or 'OpenAIRE')</li> <li>Similarity: semantic similarity value of the two categories</li> <li>PapersInC1: number of papers from the collected dataset belonging to the first category</li> <li>PapersInC2: number of papers from the collected dataset belonging to the second category</li> <li>PapersInBoth: number of papers from the collected dataset belonging to both of the categories</li> <li>Agreement: the value of the agreement of the categories in the tw KGs (Intersection over Union)</li> </ul>
Analysis materials for "Defininig a Knowledge Graph Development Process through a Systematic Review"
<p>This depository stores the analysis materials for the article "Defining a Knowledge Graph Development Process through a Systematic Review". It includes:</p> <ul> <li><strong>Analysis of KG development process - Articles.csv </strong>- a table of summary of the articles included in the systematic review.</li> <li><strong>Analysis of KG development process - Tasks by level (count).csv</strong> - a table of counting the frequency of the tasks in the knowledge graph development process.</li> <li><strong>Analysis of KG development process - Tasks by level count (synonyms) (1).csv </strong>- a table of counting the frequency of the tasks in the knowledge graph development process after its been adjusted to synonyms.</li> <li><strong>Knowledge graph development processes - </strong>a folder of process figures from the articles that have been included in the systematic review.</li> </ul>
A hierarchical graph-based model for mobility data representation and analysis
<p>Hierarchical representations of transportation networks should provide a better understanding of mobility patterns and the underlying structures at various abstraction levels. A hierarchical graph-based model allows representing moving objects and trajectories according to multiple spatial, temporal and semantic scales. The latter model is implemented here in a Neo4j graph database (version 4.4.0) and experimented with historical maritime data covering Brittany Bay in France.</p>
Graph 1 in Gastroesophageal tube of the Iguana iguana (Iguanidae): histological description, histochemical and immunohistochemical analysis of 5-HT and SS cells
Graph 1. Number of 5-HT and SS-cells/µm2 per oesophageal and gastric region in I. iguana. Values are averages with their standard deviatin shown by vertical bars (p = 0,05). Cervical oesophagus (CER): 4.6x10-2 ± 2.0 [5-HT cells]/µm2. Celomatic oesophagus (CEL): 4.0x10-2 ± 1.0 [5-HT cells]/µm2. Cranial and middle regions of the stomach (C/MS): 6.18x10-2 ± 3.2 [5-HT cells]/µm2. Caudal Stomach (CS): 0.6x10-2 ± 0.2 [5-HT cells]/µm2 and 1.4x 10-2 ± 0.9 [SS-cells]/µm2.
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>
Internet AS-Graph Edgelist for Network Analysis
<p>This edgelist serves network analyses.</p>
Combat-TB-NeoDB: fostering Tuberculosis research through integrative analysis using graph database technologies.
<p>NeoDB is a free and open source integrated M.tuberculosis ‘omics’ knowledge-base. NeoDB is based on Neo4j and enables researchers to execute complex federated queries by linking well-known, curated and widely used biological data resources, and supplementary TB variants data from published literature.</p> <p>Documentation can be found at https://combat-tb-db.readthedocs.io</p>
Graph Laplacians used in the article "Numerically Efficient $H_{\infty}$ Analysis of Cooperative Multi-Agent Systems"
<p>The repository contains four graph Laplacians written in the Matlab format. These matrices are used in the section "Numerical Examples" in the article "Numerically Efficient $H_{\infty}$ Analysis of Cooperative Multi-Agent Systems".</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Graph Files
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding graph files of the extracted Zooniverse networks described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The graph files are in <code>.gexf</code> (graph exchange XML format) and <code>.gml</code> (graph modeling language) formats which can be used by common graph/network-analysis and visualisation tools such as Gephi.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations of the network structure, involving additional (not yet analysed) features such as the content of the comments etc.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains distinct graph files for each of the analysed projects. For each graph file, there are <em>nodes </em>and <em>edges</em> and their associated attributes (i.e., each edge can have an attribute). For the edges, apart from source and target, we have as attributes:</p> <ul> <li><code>weight</code></li> <li><code>project_title</code></li> <li><code>body </code>(i.e., text)</li> <li><code>created_at</code></li> <li><code>userRoles</code></li> <li><code>discussion_title</code></li> <li><code>discussion_id</code></li> <li><code>user_id</code></li> <li><code>board_title</code></li> <li><code>relation</code></li> <li><code>target_role</code></li> </ul> <p>For the nodes, the attributes are:</p> <ul> <li><code>user_id</code></li> <li><code>userRoles</code></li> <li><code>degree_reply </code>(i.e., degree for the reply relation)</li> <li><code>in_degree_reply</code></li> <li><code>out_degree_reply</code></li> <li><code>degree_comment</code></li> <li><code>in_degree_comment</code></li> <li><code>out_degree_comment</code></li> <li><code>degree_total</code></li> <li><code>in_degree_total</code></li> <li><code>out_degree_total</code></li> <li><code>target_role</code></li> </ul> <p><strong>Grouping:</strong></p> <p>Each graph file represents all the comments for the respective project across its lifespan irrespective of any time slices. Edges represent the comments and users represent the nodes. While the different boards are still contained within the data, all boards occur in the data.</p> <p> </p>
COJO ARG variants from "Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits"
<p>These are COJO ARG variants accompanying the manuscript "Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits". For more details, view the README.md file and refer to our manuscript.</p>
Sequence data from viral assembly graph analysis of the SERC virome
<p>Various sequence data and details that were used in publishing the manuscript describing assembly graph binning in the SERC viral metagenome. Including: assembled contigs, FASTG, predicted ORFs, two tables describing the assembled contigs and graphs.</p> <p>Also PolA and RNR sequences that were mined from the SERC assembly.</p>
Epigenomic analysis on the HPRC genome graphs
<p>Processed data, analysis and figure code for the epigenomic analysis on the HPRC genome graph.</p>
Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 1000 samples
<p>Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 1000 samples</p> <p>unzip, run data_set.py to see how to interact with the dataset using PyTorch Geometric toolbox</p>
Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 6000 samples
<p>Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 6000 samples</p> <p>unzip, run data_set.py to see how to interact with the dataset using PyTorch Geometric toolbox</p>
Pangenome graph analysis reveals extensive effector copy-number variation in spinach downy mildew
<p>Data produced for the comparison of six <em>Peronospora effusa</em> isolates. For each isolate, we provide the genome assemblies, gene and repeat annotation, effector clustering, and gene variation. Additionally, we provide the repeat library that was used to annotate the transposable elements for each isolate and the pangenome graph.</p> <p>DOI: https://doi.org/10.1101/2024.05.30.596583 </p>
Gramtools enables multiscale variation analysis with genome graphs
<p>Singularity container and data files supporting the article.</p> <p>Instructions to use these data are at: https://github.com/iqbal-lab-org/paper_gramtools_nesting </p>
Call Graph Soundness in Android Static Analysis
<div> <pre>## Artifact Folder Structure Below is a brief explanation of each directory of our artifacts: - **dataset/**: Contains the dataset used in our study. - **dynamic_analysis/**: Contains everything needed to reproduce our dynamic analysis experiments. - **static_analysis/**: Contains everything needed to reproduce our static analyses experiments. - **instrumentation/**: Contains the necessary files to instrument the apps for the dynamic analysis. - **SLR/**: Contains the excel files with the papers collected during our Systematic Literature Review (SLR). Please ensure that all the necessary files and resources are present in the respective directories before running any experiments.</pre> </div>
Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction
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