of these transactions are of cryptocurrency value transfers, related data used in the tools, such as transaction graphs. This chapter describes a graph-based method for analyzing the identity The analysis, which is performed on two years of Bitcoin transaction data. Using the blockchain data of each of these currencies, the transactions in which they occur can be accessed. As a result, it is possible to analyze transactions. ETHEREUM USES BLOCKCHAIN
Create Vertices, Edges and construct the transaction Graph from it. Once the graph is in memory, analyze user activity such as:. PageRank measures the importance of a vertex i. It is implemented either with controlled iterations or allowing it to converge. Investigation shows this account processes asset exchanges, which explains the high activity.
The notebook converts the graph tx data into an NxN adjacency matrix, and each vertex is assigned a unique color along the circumference. The chart uses the first 6 char of the account ids for readability. Figure 6: Account Interaction using D3 Chord.
A: Distinct clusters form around the most active accounts. B: Different types of graphs can be constructed—account to account with the nodes representing the accounts and edges representing the transactions. These are directed graphs with the arrows going from sender to receiver. The thickness of the edge is an indication of the volume of traffic. C: Another method would involve giving each asset a different color to see how the various assets interact.
The diagram above shows a subset of the assets and the observation is that they are generally distinct with some overlap. D: Zooming into the vortex displays the account id, which aligns with the top senders from the Graph APIs. Queries can be saved in a catalog for reuse and are cached for quicker execution. These queries can be parameterized and set to refresh on an interval and are the building blocks of dashboards that can be created quickly and shared.
Like the queries, the dashboards can also be configured to automatically refresh with a minimum refresh interval of a minute and alert the team to meaningful changes in the data. Tags can be added to queries and dashboards to organize them into a logical entity. High-level blockchain stats Provides a general overview of key aggregate metrics indicating the health of the blockchain. Algo Price and Volume Monitors the Algo cryptocurrency price and volume for correlation with blockchain stats.
Block Trends Provide a historical view of the number of transactions per block and the time required to produce each block. Transaction Trends Provides a more detailed analysis of transaction activity, including volume, transaction type, and assets transferred. Account Activity Provides a view of account behavior, including the most active accounts and the assets transferred between them.
Section 1: High-level blockchain stats This section is a birds-eye view of aggregate stats, including the count of distinct asset types, transaction types, active accounts in a given time period. Figure 8: High-level details of the Algorand Blockchain. A : The cumulative number of active accounts in a given time period B : The average number of transactions per block in the given time period C : The cumulative number of distinct assets used in the given time period D : The cumulative number of distinct transaction types in the given time period E : A word cloud representing the top trending words extracted from the note field of the transactions F : An alphabetic listing of the asset types.
Each asset has a unique identifier, a unit name, and the total number of assets available. Section 2: Algo price and volume This section provides price and volume data for Algos, the Algorand cryptocurrency. The price and volume are retrieved using the CryptoCompare API to correlate with the transaction volume. Figure 9: Price and Volume data for Algos. A : Shows trading details daily Algo price on left axis and volume traded on right axis since a given date B : Shows the same on an hourly basis for a given day.
In that case, it could indicate that the underlying blockchain nodes may not be functioning optimally. A : The latest Block number B : Number of transactions in the most recent block C : Time in seconds for the block to be created D : The distribution of transaction types in this block E : The asset type distribution for each transaction type within this block.
Pay transactions use Algo and are not associated with an asset type. F : The individual transactions within this block Section 4: Block trends This section is an extension of the previous and provides the historical view of the number of transactions per block and the time required to produce each block. Figure Per block trends. A : The number of transactions per block has a few spikes but shows a regular pattern. Transaction volume is significant since it reflects user adoption on the Algorand blockchain.
B : The time in seconds to create a new block is always less than 5 seconds. The latency indicates the health of the blockchain network and the efficiency of the consensus protocol. An alert monitors this critical metric to ensure that it remains below 5 seconds.
Figure Configuring thresholds for Alert notifications. Section 5: Transactions trends This section provides a more detailed analysis of transaction activity, including volume, transaction type and assets transferred. Figure Trends in Transactions. A : Asset statistics count, average, min, max, sum on the amount by hour and asset type B : For a given day, the trend of the transaction count by hour C : Average transaction volume by hour across the entire time period.
There is a pattern that is similar to the previous. D : The distribution of transaction types on a daily basis shows a high number of asset transfers axfer followed by payments with Algos pay E : The transaction type distribution is the same for the selected day F : The transaction volume distribution by asset type G : The transaction volume by asset id over time on a daily basis. YouNow and Planet are the top asset ids traded in the given period. H : The max transaction amount by asset id over time on a daily basis.
Section 6: Account activity This section provides a view of current account activity, including the most active accounts and the assets transferred. A Sanky diagram illustrates the flow of assets between the most active accounts. Figure Top Accounts by transaction volume. A : Top Senders by transaction volume B : Daily transaction volumes of the identified top 20 senders C : Sankey Diagrams are useful to capture behavioral flows and sequences.
How the transactions flow on either side tell the bigger story and help us understand the hidden nuances of a large source or sink account and contributors along the path. Machine learning practitioners and data analysts perform multiple types of analysis on all the data on a single platform in place. Graph algorithms are applied to analyze account behavior. With SQL Analytics, business analysts derive better business insights through powerful visualizations using queries directly on the data lake.
Thank you for signing up! Our latest blogs will come directly to your inbox. By design, this database and its updates are public to allow a real-time majority consensus to form as to the current valid system state. In this way, through the elegant coupling of cryptography with economic incentives, participating pseudonymous strangers are able to establish mutual trust and conduct secure transactions among themselves with high confidence.
Within the Bitcoin network, several protocol conformant data structures are propagated around the peer-to-peer network using a gossip algorithm. The entire Bitcoin system exists exclusively to create, propagate, verify, and record data structures known as transactions. A transaction is an atomic record through which ownership of an amount of bitcoin is transferred by the current owner to a new owner. A transaction is composed of 1. Transaction outputs are new records of amounts of bitcoin along with an associated encumbrance to a particular Bitcoin address , being a representation of the public key component of an asymmetric cryptographic challenge satisfiable only by the new owner.
Transaction inputs are pointers to existing unspent transaction outputs UTXO's along with a valid proof of the particular UTXO's existing cryptographic challenge to verifiably demonstrate ownership. It is only through the provision of all the input solutions to the cryptographic challenges that a transaction will be recognized and recorded by participants as valid, preventing theft. Similarly any transaction attempting to reassign ownership of previously unencumbered amounts double spend or include outputs summing to more than the inputs counterfeiting will be rejected by the majority of honest participants.
Each new transaction's unspent outputs can therefore be considered the frontier edge of a particular tree of spends through the entire transaction graph, rooted at a set of coinbase transactions. Transactions are broadcast around the network and each participating node will keep a copy of received transactions that it considers valid in a data structure held in volatile memory known as the mempool. Specialist nodes on the peer-to-peer network known as miners proceed to select a set of transactions of their choosing from their own mempool and package them into a data structure known as a block.
By including a special reward to themselves known as a coinbase transaction, a miner will generate a block header summarizing this static transaction data set along with some metadata, including a reference to the previous valid block. The miner will then set about solving a variable nonce field in a sequential brute-force manner such that the block header's cryptographic fingerprint satisfies the current network-wide difficulty criteria. Once a miner finds a winning solution to this lottery whose difficulty is amended approximately every 2 weeks to result in an average block solution every 10 minutes and the probability of winning such is directly proportional to the amount of processing power invested in the lottery , the block is broadcast around the network to be checked by each node against a set of validation criteria.
If the block and every transaction contained therein are conformant to the agreed protocol, each full node on the network will add the block to its own independent local copy of the blockchain. All miners will then commence a new race to solve a block of the next transaction set. Thus, a network-wide consensus on the valid system state is reached, and any node can recreate the current consensus system state independently. By its nature, anyone participating in the network has access to all data in binary form through TCP connections to neighboring nodes.
In generating our visualizations, however, we chose to use some of the many curated and generously free feeds from Bitcoin data providers, particularly Blockchain. The granular and public nature of the Bitcoin dataset presents a unique opportunity for the study of a closed economic system at such scale and has already attracted much analysis. The first interesting deployment of small-scale visualization to directly analyze transaction data in the blockchain is presented by Di Battista et al.
With M pixels at our disposal, our motivation was to generate a top-down system-wide visualization to explain Bitcoin to a lay audience and begin an explorative analysis of algorithmic patterns of associated behaviors in the transaction data. The Bitcoin blockchain, with its canonical ordering of sequences of transactions and associations between spending addresses, naturally lends itself to graph visualization and that is the focus of our work.
However, faced with the large size of the full transaction graph described in Table 1 , any visualization effort is forced to compromise between which discrete subset of data to visualize and how to abstract away unnecessary detail. Previous bottom-up approaches have achieved this by restricting the scope of their analyses to identifying a limited subset of starting points of interest in the blockchain from which to visualize.
Address-based graph visualizations have typically been separated from transaction-based graphs. Furthermore, details of the particular associations in transaction graphs are usually abstracted away into summary form. Specifically a transaction is the only type of node represented in typical transaction graph visualizations, with its edge associations between its inputs and any number of other transactions and their outputs abstracted to a single-labeled edge between transaction nodes.
While retaining enough information for quantitative analysis, the visual fidelity to the underlying data is much reduced. Concretely, visually identifying a transaction with an unusually large number of outputs or an anomalous amount of Bitcoin sourced from a previous transaction becomes an arduous visual operation on textual data in such abstracted form.
Bitcoin blockchain summary statistics at the 7th year anniversary of the genesis block on January 3, With the full benefit of the large-scale digital canvas available in our data observatory, our visualization goal was to remain as faithful to the underlying data as possible to retain the richest observational insight into the identification of anomalies and patterns of behavior.
In particular, we found it important to retain visual impact regarding the input and output structure of a transaction, the relative value of transactions, and to maintain associations between both transactions and addresses within the scope of a single visualization. We chose to restrict our subset of blockchain data based on sequential series of blocks without abstraction.
To layout our graph in a force-directed minimum energy equilibrium state to visually discern its structure, we used the continuous ForceAtlas2 12 algorithm available in the SigmaJS 13 library. The implementation provides for Barnes—Hut optimization familiar to n -body simulations to reduce the computational complexity from O N 2 to O NlogN.
To that end, the basic design of our graph visualization is as follows:. A transaction node's only purpose though is to provide a local focus for its associated inputs and outputs. They are associated to their containing transaction by an orange edge.
They are associated to their containing transaction by a blue edge and if an output should become referenced as an input in a subsequent transaction within the scope of the visualization, it is joined to that transaction by an orange input edge, thus forming a chain of spends Fig.
Visualizing a simple chain of spends in the mempool with blue outputs from one transaction becoming orange inputs to the next, from a source coinbase transaction in red. It can be seen from the stylized representation shown in Figure 3 that all contextual and association information from the transaction data structure can be visualized in one graph and thus any amounts, structures of individual transactions, high-frequency chains of spends, or address associations of an anomalous nature will be immediately apparent by visual inspection.
Stylized transaction visualization sourcing five equal input amounts from a single address and paying 25BTC to a new address. We now take our transaction representation and apply it to an animated graph whose layout evolves in real time to visualize transactions and their associations as they are broadcast into the network and join all peers' mempools. Furthermore, we apply the same animated force-directed visualization to explore individual blocks of static data laid out on request to explore past behaviors.
To gently introduce a lay audience to some of the abstract concepts of Bitcoin, we also produced a global visual manifestation of the activity on the peer-to-peer network, less intimidating in its complexity. By interacting with the Bitcoin network through known stained addresses, it is also possible to conduct an active data analysis by identifying one's own transactions and the network's responses.
A High-resolution 8k visualization of a standard block; B detail of both a low small node and a high large node value transaction, C known and linked Bitcoin addresses, D a payout system, and E a highly associated disconnected component believed to be a coin-tumbling service to move amounts rapidly between addresses, obfuscating the source and destination of funds. Independent transactions are visually associated to each other in two ways: either directly through an existing output becoming an input to a new transaction within the timeframe of the visualization or indirectly through the reuse of the same cryptographic public key within an element of a transaction, which we connect with a gray edge.
Interacting with the visualization is simple. We provide for pan, zoom, and hover over methods to display uncluttered textual data such as transaction references and address information. We facilitate further detailed data analysis by highlighting connected components along with the ability to transmit such subcomponent data in JSON by PeerJS to hand-held tablet displays for a more detailed, localized analysis directly linked to online Bitcoin exploration tools such as Blockchain.
Filtering the visualized data set by amount, address, or reference is also possible from the hand-held tablet display. The current Bitcoin transaction rate under normal circumstances is around 2—3 per second. A typical simple transaction, as shown in Figure 4B , will be rendered in our visualization with four vertices the transaction, an input, a spending output, and an output back to the current owner for an amount of change.
This enables scalability to explore historical transactions. We store an index of the latest transactions in a circular buffer, which when full removes the oldest transactions from the visualization on a First-In—First-Out basis. Transactions are also removed from this visualization should they be included in any block as it is broadcast into the network.
In this way, computational load in rendering the layout is continuously managed such that the number of nodes in the visualization is never more than around 10, given the multiple inputs and outputs associated with each transaction. This visualization is similar in nature to the mempool, but provides the ability to visually explore any individual block mined into the blockchain.
It allows the visual recognition of recurring patterns within the average minute timeframe of a block. Special coinbase transactions rewarding miners which are not broadcast in the network and thus inapplicable to the mempool visualization have no source inputs since they are newly minted coins and are visualized here in red.
Visualizing blocks ,, previously reported as containing anomalous yet unidentified transactions at the apex of a money laundering operation, 21 demonstrating ease of visual search and hover-over interaction for isolation and further analysis. Expanded later in this article, this visualization has allowed us to detect anomalous high-frequency behavioral patterns within the Bitcoin transaction graph and demarcate a period of artificial network stress into two distinct and independent behaviors that were previously hidden in the dense raw dataset.
Building on previous analysis, 10 section 3. Figure 5 shows the ease with which our tool allows immediate visual identification of these transactions, given knowledge only of their anomalous nature. The aim of this simple rotating globe visualization, shown in Figure 6 , was to demonstrate the global scope of the peer-to-peer network and bring to life areas of activity. Knowledge of network topology is not only important to ensure network robustness and efficient data propagation but also to determine which nodes may have an advantage and which attacks on the system may be feasible.
Global visualization of contactable nodes and transaction activity on the Bitcoin peer-to-peer network. A Bitcoin Core node cold booting into the P2P network embarks on a process of network discovery through the use of hardcoded DNS servers; it subsequently maintains knowledge of up to peers in its local addrMan database through the gossip of ADDR messages despite only initiating a maximum of eight actual peer connections. By recursively attempting ingoing connection attempts to all endpoints observed in the exchange of ADDR messages, it is possible to spider through the subset of nodes forming the backbone network of contactable peers.
Using data from Blockchain. We have found that this visualization greatly aids in the lay explanation of a peer-to-peer overlay network and the global nature of Bitcoin infrastructure and its activity. In this case, however, the transactional insight the visualization provides is of limited value since it is dependent on the particular latencies and connections of the Blockchain.
With the addition of topological data derived from Miller et al. While conducting this work and exploring the mempool on a daily basis over the summer of , a sustained attack upon the Bitcoin network became immediately visible and warranted further investigation:. A long-running source of disagreement within the Bitcoin community is the arbitrary 1 MB limit on the size of a block.
Originally implemented to prevent certain denial of service attacks, it prevents the system from scaling beyond a transaction rate of only around four transactions per second. In , unknown actors took it upon themselves to automatically generate economically insignificant spam transactions, in an effort to artificially increase the data rate and seemingly press home the need to raise the 1 MB limit.
By visualizing these transactions mined into blocks over that period, it is possible to make several observations of interest. Processing this volume of transactions occupied network resources and caused a degradation in the service of regular transactions. Similar in nature to throwing a handful of dollar bills into a crowded room, we quickly observed the algorithmic scramble to collect these multiple small amounts of Bitcoin, including the mining of the largest possible single transaction at 1 MB in Figure 8.
Blocks , Initial algorithmic responses to spam, the lower block showing the largest possible transaction. This transaction rate attack forming the parasitic worm structures persisted across many blocks. It caused delays in the processing of all transactions and a backlog of transactions in the mempool pending verification.
However, even after the transaction rate returned to normal, it was evident that the network was still under duress. Figure 9 shows the sudden single increase in transaction rate, but only on inspection of the average block size does it become apparent that a second attack occurred in quick succession, the nature of which was data density rather than transaction rate.
Network statistics showing the change from a transaction rate attack to the two-phased data density attack. This second attack occurred in two phases as shown by the change in gradient of the number of records in the UTXO set in Figure 9.
The attack had a limited impact on the backlog of transactions in the mempool, but a very pernicious effect on the number of UTXOs. This attack is very much one of data density rather than transaction rate and probably conducted by an entirely separate second party.
It is also obvious to note the point at which a simple constant parameter in the algorithm was amended to increase the data density of this attack in its second phase, shown in Figure Many of these insights arose from collaborative discussions among multidisciplinary researchers within the immersive visualization environment of the data observatory, which allowed the details of these visualizations to be interrogated as a group.
This is where the advantage of rendering into a high-definition large-scale observatory proves its worth. Not only is the human visual system able to easily discern the associated patterns of behavior observable in the data but one can also physically approach the detail in the data and conduct a fine-grained analysis of one particular anomaly, while maintaining the context of the whole picture.
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