The global graph database market was valued at USD 1.93 billion in 2021 and is expected to grow at a CAGR of 23.1% during the forecast period.

This dataset contains bitcoin transfer transactions extracted from the Bitcoin Mainnet blockchain. Details of the datasets are given below: FILENAME FORMAT: The filenames have the following format: btc-tx- where For example file btc-tx-100000-149999-aa.bz2 and the rest of the parts if any contain transactions from block 100000 to block 149999 inclusive. The files are compressed with bzip2. They can be uncompressed using command bunzip2. TRANSACTION FORMAT: Each line in a file corresponds to a transaction. The transaction has the following format: BLOCK TIME FORMAT: The block time file has the following format: IMPORTANT NOTE: Public Bitcoin Mainnet blockchain data is open and can be obtained by connecting as a node on the blockchain or by using the block explorer web sites such as https://btcscan.org . The downloaders and users of this dataset accept the full responsibility of using the data in GDPR compliant manner or any other regulations. We provide the data as is and we cannot be held responsible for anything. NOTE: If you use this dataset, please do not forget to add the DOI number to the citation. If you use our dataset in your research, please also cite our paper: https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14 @incollection{kilicc2022analyzing, title={Analyzing Large-Scale Blockchain Transaction Graphs for Fraudulent Activities}, author={K{\i}l{\i}{\c{c}}, Baran and {"O}zturan, Can and {\c{S}}en, Alper}, booktitle={Big Data and Artificial Intelligence in Digital Finance}, pages={253--267}, year={2022}, publisher={Springer, Cham} }

United States graph database market size reached USD 537.9 Million in 2024. Looking forward, IMARC Group expects the market to reach USD 2,754.7 Million by 2033, exhibiting a growth rate (CAGR) of 19.7% during 2025-2033​. The widespread adoption of this innovative approach, as it offers numerous advantages over traditional database solutions in terms of computing power, storage, indexing, querying, etc., is primarily driving the market growth across the country.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for ​2025-2033​. Our report has categorized the market based on component, type of database, analysis type, deployment model, application, and industry vertical.

The Graph Definition resource provides a formal computable definition of a graph of resources - that is, a coherent set of resources that form a graph by following references. The Graph Definition resource defines a set and makes rules about the set.

[198+ Pages Report] Global graph database market size & share estimated to be worth USD 5.2 Billion in the year 2026, growing at a CAGR value of 21.7% during the forecast period of 2021-2026.

Уникальный идентификатор https://doi.org/10.5281/zenodo.4718440 Набор данных обновлен Dec 19, 2022 Набор данных предоставлен Zenodo Авторы Can Özturan; Can Özturan; Alper Şen; Alper Şen; Baran Kılıç; Baran Kılıç Лицензия Attribution 4.0 (CC BY 4.0) Информация о лицензии была получена автоматически Описание This dataset contains ether as well as popular ERC20 token transfer transactions extracted from the Ethereum Mainnet blockchain. Only send ether, contract function call, contract deployment transactions are present in the dataset. Miner reward (static block reward) and "uncle block inclusion reward" are added as transactions to the dataset. Transaction fee reward and "uncles reward" are not currently included in the dataset. Details of the datasets are given below: FILENAME FORMAT: The filenames have the following format: eth-tx- where For example file eth-tx-1000000-1099999.txt.bz2 contains transactions from block 1000000 to block 1099999 inclusive. The files are compressed with bzip2. They can be uncompressed using command bunzip2. TRANSACTION FORMAT: Each line in a file corresponds to a transaction. The transaction has the following format: units. ERC20 tokens transfers (transfer and transferFrom function calls in ERC20 contract) are indicated by token symbol. For example GUSD is Gemini USD stable coin. The JSON file erc20tokens.json given below contains the details of ERC20 tokens. Failed transactions are prefixed with "F-". BLOCK TIME FORMAT: The block time file has the following format: erc20tokens.json FILE: This file contains the list of popular ERC20 token contracts whose transfer/transferFrom transactions appear in the data files. ERC20 token list: USDT TRYb XAUt BNB LEO LINK HT HEDG MKR CRO VEN INO PAX INB SNX REP MOF ZRX SXP OKB XIN OMG SAI HOT DAI EURS HPT BUSD USDC SUSD HDG QCAD PLUS BTCB WBTC cWBTC renBTC sBTC imBTC pBTC IMPORTANT NOTE: Public Ethereum Mainnet blockchain data is open and can be obtained by connecting as a node on the blockchain or by using the block explorer web sites such as http://etherscan.io . The downloaders and users of this dataset accept the full responsibility of using the data in GDPR compliant manner or any other regulations. We provide the data as is and we cannot be held responsible for anything. NOTE: If you use this dataset, please do not forget to add the DOI number to the citation. If you use our dataset in your research, please also cite our paper: https://link.springer.com/article/10.1007/s10586-021-03511-0 @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} }

The global graph database market size is expected to reach USD 11.60 billion by 2030 according to a new study by Polaris Market Research.

The Global Graph Database Market, valued at USD 3.12 billion in 2024, is projected to grow at a 23.56% CAGR from 2025-30, driven by AI tools and low-latency query processing.

Graph Database Market Report is Segmented by Component (Solutions, Services [Managed, Professional]), Deployment (Cloud On Premises), Size (SMES, Large Enterprises), End User (BFSI, Healthcare, Retail & E-Commerce, IT and Telecom, Media and Entertainment, Other End Users), Geography (North America, Europe, Asia-Pacific, Rest of the World). The Report Offers Market Forecasts and Size in Value (USD) for all the Above Segments.

The global knowledge graph technology market is projected to reach a value of USD 4.7 billion by 2033, exhibiting a CAGR of 10.3% from 2025 to 2033. The surge in data volume and the increasing adoption of artificial intelligence (AI) and machine learning (ML) are the key factors driving the growth of this market. The increasing need for effective data management and analysis is also contributing to the market's expansion. Key market trends include the shift towards unstructured knowledge graphs, the integration of knowledge graphs with natural language processing, and the increasing use of knowledge graphs in enterprise applications. Based on type, the market is segmented into structured knowledge graphs and unstructured knowledge graphs. Structured knowledge graphs are more common and are used in a wide range of applications, including search engines, question answering systems, and recommender systems. Unstructured knowledge graphs are less common but are becoming increasingly popular as they can represent more complex and nuanced relationships. Based on application, the market is segmented into medical, finance, education, and others. The medical segment is the largest and is expected to continue to grow as knowledge graphs are used to improve patient care and outcomes. The finance segment is also growing rapidly as knowledge graphs are used to improve risk management, fraud detection, and customer segmentation. The education segment is also growing as knowledge graphs are used to improve student learning and engagement.

Global Graph Technology Market Share size & share value expected to touch USD 23.48 billion by 2032, to grow at a CAGR of 21.9% during the forecast period.

According to Cognitive Market Research, the global Graph Analytics market size will be USD 2522 million in 2024 and will expand at a compound annual growth rate (CAGR) of 34.0% from 2024 to 2031. Market Dynamics of Graph Analytics Market

Key Drivers for Graph Analytics Market

Increasing Recognition of the Advantages of Graph Databases- One of the main reasons for the Graph Analytics market is the increasing recognition of the advantages of graph databases. Unlike traditional relational databases, graph databases excel at handling complex relationships and interconnected data, making them ideal for use cases such as fraud detection, recommendation engines, and social network analysis. Businesses are leveraging these capabilities to uncover insights and patterns that were previously difficult to detect. The rise of big data and the need for real-time analytics are further driving the adoption of graph databases, as they offer enhanced performance and scalability for large-scale data sets. Additionally, advancements in artificial intelligence and machine learning are amplifying the value of graph databases, enabling more sophisticated data modeling and predictive analytics.
Growing Uptake of Big Data Tools to Drive the Graph Analytics Market's Expansion in the Years Ahead.

Key Restraints for Graph Analytics Market

Limited Awareness and Understanding pose a serious threat to the Graph Analytics industry.
The market also faces significant difficulties related to data security and privacy.

Introduction of the Graph Analytics Market

The Graph Analytics Market is rapidly expanding, driven by the growing need for advanced data analysis techniques in various sectors. Graph analytics leverages graph structures to represent and analyze relationships and dependencies, providing deeper insights than traditional data analysis methods. Key factors propelling this market include the rise of big data, the increasing adoption of artificial intelligence and machine learning, and the demand for real-time data processing. Industries such as finance, healthcare, telecommunications, and retail are major contributors, utilizing graph analytics for fraud detection, personalized recommendations, network optimization, and more. Leading vendors are continually innovating to offer scalable, efficient solutions, incorporating advanced features like graph databases and visualization tools.

Graph Analytics Market size was valued at USD 77.1 Million in 2023 and is projected to reach USD 637.1 Million by 2030, growing at a CAGR of 35.1% during the forecast period 2024-2030.

Global Graph Analytics Market Drivers
The market drivers for the Graph Analytics Market can be influenced by various factors. These may include:

Growing Need for Data Analysis: In order to extract insightful information from the massive amounts of data generated by social media, IoT devices, and corporate transactions, there is a growing need for sophisticated analytics tools like graph analytics.

Growing Uptake of Big Data Tools: Graph analytics solutions are becoming more and more popular due to the spread of big data platforms and technology. Businesses are using these technologies to improve the efficiency of their analysis of intricately linked datasets.

Developments in AI and ML: The capabilities of graph analytics solutions are being improved by advances in machine learning and artificial intelligence. These technologies make it possible for recommendation systems, anomaly detection, and forecasts based on graph data to be more accurate.

Increasing Recognition of the Advantages of Graph Databases: Businesses are realizing the advantages of graph databases for handling and evaluating highly related data. Consequently, there’s been a sharp increase in the use of graph analytics tools to leverage the potential of graph databases for diverse applications.

The use of advanced analytics solutions, such as graph analytics, for fraud detection, cybersecurity, and risk management is becoming more and more important as a result of the increase in cyberthreats and fraudulent activity.

Demand for Personalized suggestions: Companies in a variety of sectors are using graph analytics to provide their clients with suggestions that are tailored specifically to them. Personalized recommendations increase consumer engagement and loyalty on social networking, e-commerce, and entertainment platforms.

Analysis of Networks and Social Media is Necessary: In order to comprehend relationships, influence patterns, and community structures, networks and social media data must be analyzed using graph analytics. The capacity to do this is very helpful for security agencies, sociologists, and marketers.

Government programs and Regulations: The need for graph analytics solutions is being driven by regulations pertaining to data security and privacy as well as government programs aimed at encouraging the adoption of data analytics. These tools are being purchased by organizations in order to guarantee compliance and reduce risks.

Emergence of Industry-specific Use Cases: Graph analytics is finding applications in a number of areas, such as healthcare, finance, retail, and transportation. These use cases include supply chain management, customer attrition prediction, and financial fraud detection in addition to patient care optimization.

Technological Developments in Graph Analytics Tools: As graph analytics tools, algorithms, and platforms continue to evolve, their capabilities and performance are being enhanced. Adoption is being fueled by this technological advancement across a variety of industries and use cases.

An oil supply graph visually represents the historical and projected data related to the production and availability of oil over a given period of time. It is an essential tool for policymakers, economists, and analysts to understand and predict trends in the oil market.

The Graph Technology Market is projected to grow at 22.0% CAGR, reaching $14.21 Billion by 2029. Where is the industry heading next? Get the sample report now!

RDF dump of the Words Matter knowledge graph, which contains Dutch and English contentious terms often found in museum databases. Using the CULCO ontology, this knowlege graph models relationships between contentious terms, explanations, suggestions on their usage, and alternative terms given by experts from the cultural sector.

See related paper: A Knowledge Graph of contentious terminology for inclusive representation of cultural heritage.

As of 2020, the overall size of the knowledge graph market in China reached roughly 35 billion yuan and was projected to grow at an average compound annual rate of 20 percent over the observed period, amounting to over 130 billion yuan in 2026. In the AI segment knowledge graphs describe a machine learning technique.

Dataset Construction 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 (https://github.com/blockchain-etl/bitcoin-etl) 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 https://www.walletexplorer.com/ [1] M. Harrigan and C. Fretter, "The Unreasonable Effectiveness of Address Clustering," 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), Toulouse, France, 2016, pp. 368-373, doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0071.keywords: {Online banking;Merging;Protocols;Upper bound;Bipartite graph;Electronic mail;Size measurement;bitcoin;cryptocurrency;blockchain}, Dataset Description Bitcoin Activity Temporal Coverage: From 03 January 2009 to 25 January 2021 Overview: This dataset provides a comprehensive representation of Bitcoin exchanges between entities over a significant temporal span, spanning from the inception of Bitcoin to recent years. It encompasses various temporal resolutions and representations to facilitate Bitcoin transaction network analysis in the context of temporal graphs. Every dates have been retrieved from bloc UNIX timestamp and GMT timezone. Contents: The dataset is distributed across three compressed archives: All data are stored in the Apache Parquet file format, a columnar storage format optimized for analytical queries. It can be used with pyspark Python package. orbitaal-stream_graph.tar.gz: The root directory is STREAM_GRAPH/ Contains a stream graph representation of Bitcoin exchanges at the finest temporal scale, corresponding to the validation time of each block (averaging approximately 10 minutes). The stream graph is divided into 13 files, one for each year Files format is parquet Name format is orbitaal-stream_graph-date-[YYYY]-file-id-[ID].snappy.parquet, where [YYYY] stands for the corresponding year and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering These files are in the subdirectory STREAM_GRAPH/EDGES/ orbitaal-snapshot-all.tar.gz: The root directory is SNAPSHOT/ Contains the snapshot network representing all transactions aggregated over the whole dataset period (from Jan. 2009 to Jan. 2021). Files format is parquet Name format is orbitaal-snapshot-all.snappy.parquet. These files are in the subdirectory SNAPSHOT/EDGES/ALL/ orbitaal-snapshot-year.tar.gz: The root directory is SNAPSHOT/ Contains the yearly resolution of snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-file-id-[ID].snappy.parquet, where [YYYY] stands for the corresponding year and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering These files are in the subdirectory SNAPSHOT/EDGES/year/ orbitaal-snapshot-month.tar.gz: The root directory is SNAPSHOT/ Contains the monthly resoluted snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-[MM]-file-id-[ID].snappy.parquet, where [YYYY] and [MM] stands for the corresponding year and month, and [ID] is an integer 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 These files are in the subdirectory SNAPSHOT/EDGES/month/ orbitaal-snapshot-day.tar.gz: The root directory is SNAPSHOT/ Contains the daily resoluted snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-file-id-[ID].snappy.parquet, where [YYYY], [MM], and [DD] stand for the corresponding year, month, and day, and [ID] is an integer 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 These files are in the subdirectory SNAPSHOT/EDGES/day/ orbitaal-snapshot-hour.tar.gz: The root directory is SNAPSHOT/ Contains the hourly resoluted snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-[hh]-file-id-[ID].snappy.parquet, where [YYYY], [MM], [DD], and [hh] stand for the corresponding year, month, day, and hour, and [ID] is an integer 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 These files are in the subdirectory SNAPSHOT/EDGES/hour/ orbitaal-nodetable.tar.gz: The root directory is NODE_TABLE/ Contains two files in parquet format, the first one gives information related to nodes present in stream graphs and snapshots such as period of activity and associated global Bitcoin balance, and the other one contains the list of all associated Bitcoin addresses. Small samples in CSV format orbitaal-stream_graph-2016_07_08.csv and orbitaal-stream_graph-2016_07_09.csv These two CSV files are related to stream graph representations of an halvening happening in 2016.

Explore the intricacies of maize price trends by understanding how factors such as climate conditions, global demand, and policy changes affect these graphs. Learn about the impact of both short-term and long-term influences on maize markets, including biofuel demands, seasonal cycles, and international trade dynamics.

The Enterprise Knowledge Graph Market is projected to grow at 24.4% CAGR, reaching $3.54 Billion by 2029. Where is the industry heading next? Get the sample report now!