Blockchain visualization reading

Contact: Kathryn Ryan kryan liebertpub. Mary Ann Liebert, Inc. New Rochelle, July 5, — A novel visualization method for exploring dynamic patterns in real-time Bitcoin transactional data can zoom in on individual transactions in large blocks of data and also detect meaningful associations between large numbers of transactions and recurring patterns such as money laundering. The information and insights made possible by this top-down visualization of Bitcoin cryptocurrency transactions are described in an article in Big Data , the highly innovative, peer-reviewed journal from Mary Ann Liebert, Inc. The article is available free for download on the Big Data website. Top-down system-wide visualization enables pattern detection, and it is then possible to drill down into any particular transaction for more detailed information.

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Content:

• Blockchain Technology and Supply Chain Management
• Mapping the NFT revolution: market trends, trade networks, and visual features
• Analyze cryptocurrency market data
• Visualizing the Rise of Cryptocurrency Transactions
• Visualizing individual transactions with the Bitcoin API
• Data Exploration and Visualization of Uniswap Activities with Neo4j and Motif
• Blockchain visualizations: 7 most beautiful Bitcoin visualizations

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Blockchain Technology and Supply Chain Management

Transaction screening and risk monitoring using machine learning and graph analytics. Get deeper insights on market activities and user behaviors. Business intelligence platform for graph data. Pre-transaction monitoring solution for compliance teams. API for risk scores based on crypto news sources. A walkthrough on using graph analytics to query for patterns of interest on Uniswap with the help of Bigquery, Neo4j and Motif.

Blockchain technology like Bitcoin and Ethereum provides a decentralized environment for financial transactions. Transactions are recorded in a distributed ledger aka blockchain and are immutable. This means that the entire historical data and activities on the blockchain are readily available and cannot be manipulated mathematically. These twin properties of trusted execution without a centralized authority has attracted the growth of decentralized finance activities.

These include the likes of Uniswap , a decentralized exchange or Aave , a decentralized lending platform. Our previous articles showcase some of the risks involved with some protocols. In this article, we expound on some of the exploratory techniques used in blockchain forensics and how we can analyse decentralized finance activities with Neo4j and Motif. The possibilities are really only limited to the bounds of human ingenuity.

While Bitcoin first introduced the idea of blockchain to the masses, Ethereum aims to revolutionize the world by bringing in smart contracts. This has brought on many real-world applications, highlighted in the quote above, to the Ethereum network, due to the cost efficiency and simplicity for businesses. In this article, we will be analysing Uniswap in greater detail. Uniswap is a collection of smart contract programs that functions as a decentralized exchange DEX on the Ethereum chain. Like centralised exchange CEX counterparts like Binance or Robinhood, it provides crypto trading services to users.

To keep the trading system liquid at all times, Uniswap relies on 2 groups of users. Liquidity providers loan their crypto to the liquidity pool in return for attractive yield. They are also able to withdraw their liquidity at any given time. On the other side, traders swap tokens, based on prices mathematically determined by the liquidity pool. Traders also pay trading fees which goes to the platform and the liquidity providers.

In a previous post , we showed how graph analysis can be used to understand the structure of exchanges on the Ethereum chain. For smart contract analysis, we take a similar approach to understand activities through a semantic graph structure.

We model wallets or smart contract addresses as nodes and contract calls as edges. Each of these actions also contain additional information such as the number of tokens received or transferred. This gives a rich multi-edge graph with multiple node and edge properties.

In order to get a sense of the usual patterns or activities that take place on the graph, we need tools to help us easily query and visualize such rich graph structures.

We use Neo4j as its property graph model allows us to model the rich relationships present in Uniswap data. We also leverage Motif , our open-source graph exploration tool to interactively explore the patterns and behaviours.

Uniswap data can be retrieved by directly syncing an Ethereum node and filtering on the address of interest or by querying from Bigquery. Bigquery Ethereum ETL offers a convenient entrypoint for us to analyze historical blockchain activities.

From Bigquery, Uniswap transactions can be retrieved from the blockchain-etl. Under the folder, transactions are already categorised into the corresponding tables based on their call functions. With some data cleaning and understanding of smart contract transactions, we extract out only the necessary information into standard nodes and edges format for graph visualization. For edges, there will be 3 different files corresponding to the 3 call functions. As mentioned above, we model the smart contract calls using a property graph model with nodes as wallets or smart contract addresses and edges as function calls, along with other associated properties.

We can directly query from Neo4j into Motif to explore specific parts of our dataset in greater detail. Here, we visualized 4 different liquidity pools and their users over a specific timeframe. We can clearly see that the LINK-ETH liquidity pool orange node has the most traffic based on the number of edges and nodes surrounding it. Based on the swap red and supply blue edges, it can then be inferred that the LINK-ETH pair has the highest trading volume and the largest supplied liquidity out of the pools here.

Motif allows us to add in custom filters and visualize activities over time. In the above plot, we drill down into transactions which involve either LINK or SUSHI tokens and use the variable inspector panel to filter on a subset of block numbers of interest. We can also extend the use of graph visualisation to filter out malicious and suspicious patterns. More recently, decentralized finance DeFi space in crypto has been under flash loan attacks.

If any condition as set by the trader fails in any one of the steps, the transaction will be reverted. This is used by arbitrageurs who capitalize from the price differences between DEXes. However, this has resulted in exploiters taking advantage of the coding vulnerabilities of the projects listed in the DEXes.

Pancake Bunny and Alpha Homora are some of the prominent victims, losing tens of millions from the attacks. The attack involved triggering several swaps, supply and remove liquidity call functions within a very short time window, using a combination of flash loans and normal transactions.

In the graph above, we managed to obtain 1 case based on that attack pattern. The unknown trader middle node is supplying, swapping, and removing liquidity with the Celsius liquidity pool, in addition to supplying liquidity to the Compound liquidity pool.

All of these actions are done within 15 minutes and are otherwise not observed among other traders. Including more detailed filtering conditions such as the amount of crypto involved and other attributes can help paint a more complete picture. We hope this article gives you an insight into how graph analysis is a perfect fit for blockchain forensics especially in the area of DeFi analytics.

Getting started is easy - many of the tools highlighted in this post have a free trial or open source version:. If you are interested in a more detailed walkthrough of Motif, check out our previous post and the open source github repository. If you require custom analytics and reports on the blockchain, feel free to reach us on our contact form. Motif makes visual discovery on graph datasets simple and accessible to analysts, data scientists and managers.

An investigative report explaining what cryptocurrency exit scams and DeFi rug pulls are, how they are carried out, and the tracing and investigations of such crypto frauds. Use Cases. Fraud Detection Transaction screening and risk monitoring using machine learning and graph analytics.

Blockchain Forensics Get deeper insights on market activities and user behaviors. Motif Business intelligence platform for graph data.

Transaction Monitoring Pre-transaction monitoring solution for compliance teams. Uniswap as a Graph In a previous post , we showed how graph analysis can be used to understand the structure of exchanges on the Ethereum chain. Extracting Uniswap Data Uniswap data can be retrieved by directly syncing an Ethereum node and filtering on the address of interest or by querying from Bigquery. We load a subset of the transformed dataset into neo4j for further analysis.

Using Neo4j to Analyse Uniswap Data As mentioned above, we model the smart contract calls using a property graph model with nodes as wallets or smart contract addresses and edges as function calls, along with other associated properties. Possibilities of Graph Analytics on DeFi We can also extend the use of graph visualisation to filter out malicious and suspicious patterns. Conclusion We hope this article gives you an insight into how graph analysis is a perfect fit for blockchain forensics especially in the area of DeFi analytics.

Getting started is easy - many of the tools highlighted in this post have a free trial or open source version: Data: Ethereum ETL on Bigquery 1TB of query data is free Graph database: Neo4j desktop Visualization: Motif If you are interested in a more detailed walkthrough of Motif, check out our previous post and the open source github repository.

Related Content Introducing Motif - The No-code Graph Visualization Platform Motif makes visual discovery on graph datasets simple and accessible to analysts, data scientists and managers. The Rise of Cryptocurrency Exit Scams and DeFi Rug Pulls An investigative report explaining what cryptocurrency exit scams and DeFi rug pulls are, how they are carried out, and the tracing and investigations of such crypto frauds.

Mapping the NFT revolution: market trends, trade networks, and visual features

In bestowing this status on the technology, Gartner predicted that blockchain is still five to 10 years away from going mainstream, writing:. That list is decidedly smaller, but the real-world applications of this technology that are being developed, tested, and — in some cases — rolled out to the marketplace will play a critical role in shaping the future of blockchain development and determining just how quickly the technology goes mainstream. But before any of that will really make sense, some baseline background on blockchain is required. In its simplest possible form, blockchain is a digital platform for recording and verifying transactions. The paper outlines the process of creating a purely peer-to-peer version of electronic cash that can be sent directly from one party to another without going through a financial institution. The key to maintaining the integrity of that system is a digital ledger that time-stamps transactions by logging them into an ongoing chain of record, providing proof of all transactions on the network.

Analyze cryptocurrency market data

Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address the challenges and discover new breakthroughs and trends living within this information. New Rochelle, July 5, A novel visualization method for exploring dynamic patterns in real-time Bitcoin transactional data can zoom in on individual transactions in large blocks of data and also detect meaningful associations between large numbers of transactions and recurring patterns such as money laundering. The information and insights made possible by this top-down visualization of Bitcoin cryptocurrency transactions are described in an article in Big Data , the highly innovative, peer-reviewed journal from Mary Ann Liebert, Inc. Top-down system-wide visualization enables pattern detection, and it is then possible to drill down into any particular transaction for more detailed information. The researchers describe the successful deployment of their visualization tool in a high-resolution screen data observatory facility. There is a lot of confusion about these emerging methods and a real need for articles that cut through the clutter and explain them in simple terms. Visualization is a key to understanding them.

Visualizing the Rise of Cryptocurrency Transactions

August 3, Blockchain visualization tools provide an efficient way of understanding on-chain activity and uncovering hidden relationships, anomalies, and trends from blockchain data. With these visualization tools, you enjoy enhanced transparency across blockchain networks and easily generate meaningful insights for your business processes. Crystal gives you the ability to visualize and explore crypto transactions and wallets through a super-intuitive interface. You can detect, investigate, monitor, analyze, manage, collaborate, and report on cryptocurrency activity- all in a single, slick dashboard.

Visualizing individual transactions with the Bitcoin API

The term decentralized finance DeFi refers to an alternative financial infrastructure built on top of the Ethereum blockchain. DeFi uses smart contracts to create protocols that replicate existing financial services in a more open, interoperable, and transparent way. This article highlights opportunities and potential risks of the DeFi ecosystem. I propose a multi-layered framework to analyze the implicit architecture and the various DeFi building blocks, including token standards, decentralized exchanges, decentralized debt markets, blockchain derivatives, and on-chain asset management protocols. I conclude that DeFi still is a niche market with certain risks but that it also has interesting properties in terms of efficiency, transparency, accessibility, and composability. As such, DeFi may potentially contribute to a more robust and transparent financial infrastructure.

Data Exploration and Visualization of Uniswap Activities with Neo4j and Motif

It was the first time people realized the potential of blockchain analysis to solve a criminal investigation. Today, blockchain analysis technology is used by financial institutions, crypto businesses, and law enforcement authorities to prevent and investigate cryptocurrency crimes worldwide. Cryptocurrencies are internet native currencies powered by blockchain technology. All the transactions are visible on the blockchain; however, blockchains do not contain real-world identities. Therefore associating these activities with real-world actors and stopping criminals is an ongoing challenge.

Blockchain visualizations: 7 most beautiful Bitcoin visualizations

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Cryptoasset valuation is a fairly new and iterating practice. Investors often have strong and varying opinions on how to value projects in the emerging asset class. The assets are constantly under experimentation and development, making a fixed model methodology challenging. Traditional market indicators such as market capitalization and supply are often flawed or manipulated.

Kickstarter has revealed that it is the latest tech company to try buying into the blockchain hype. The crowdfunding site is looking to switch its crowdfunding service from real-world money into blockchain. As per The Verge , the company announced that it will help aid the development of a so-called "open-source protocol" to make a decentralized version of its core service-aka crowdfunding. Kickstarter announced the switch in an official blog post, where it detailed how everything will work. It gets fairly technical there, but the main goal for the company is to commit to a "more open, collaborative, and decentralized future. One of their reasons, as per the blog, is to try to give other people on the platform a chance to get funding.

What is tezos, you ask? Tezos is a decentralized platform based on blockchain technology with its cryptocurrency XTZ. It launched in June and uses the Proof of Stake algorithm. Too much information?