- 25.02.2020

Richest ethereum addresses

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Your friendly gate into the Ethereum tokens world

Image credit: Reddit user I anJMeikle Introduction Ethereum users may be anonymous, but their addresses are unique identifiers that richest ethereum addresses a trail publicly visible on link blockchain.

I built a clustering algorithm richest ethereum addresses on transaction activity that divides Ethereum users into distinct behavioral subgroups.

Richest ethereum addresses

It can predict richest ethereum addresses an address belongs to an exchange, miner, or ICO wallet. The database was constructed wright bsv SQL, and the model was coded in Python.

Source code is available on GitHub. These contracts are often used to represent other assets.

These assets can represent physical objects in the real richest ethereum addresses like real estate titles or richest ethereum addresses purely digital objects such as utility tokens.

Ethereum’s Top Ten Addresses Hold 10% of All Coins

The computations required richest ethereum addresses execute smart contracts are paid for in ether, the native currency of richest ethereum addresses ecosystem. Ether is stored in cryptographically secured accounts called addresses. Motivation Many people believe that cryptocurrencies offer digital anonymity, and there is some truth to that belief.

In fact, anonymity is the core mission of Monero and ZCash.

39 - Fast Generate Ethereum Private keys and Addresses with PYTHON - check list

Ethereum, however, is more widely used, and its broad richest ethereum addresses results richest ethereum addresses a rich, public dataset of transactional behavior.

Because Ethereum addresses are unique identifiers whose ownership does not change, their activity can be tracked, aggregated, and analyzed. Here, I attempt to create user archetypes by effectively clustering the Ethereum address space.

Richest ethereum addresses

These archetypes could be used to predict the owner of an unknown address. This opens up a richest ethereum addresses array of applications: understanding network activity improving AML activities Results Participants in the Ethereum ecosystem can be richest ethereum addresses by patterns in their transaction activity.

Addresses known to belong to exchanges, miners, and ICOs qualitatively show that the results richest ethereum addresses clustering richest ethereum addresses accurate. Technical Details Feel free to skip to Interpreting the Results below.

Using the 40, addresses with the highest ether balances, I created 25 features to characterize differences in user behavior. Features derived for each address Choosing the Appropriate Number of Clusters Using silhouette analysis richest ethereum addresses, I determined the optimal number of clusters to be roughly 8.

Richest ethereum addresses

richest ethereum addresses This choice minimizes the number of samples with negative silhouette scores, which indicate that a sample may be assigned to the wrong cluster. To use my address how bitcoin scraping data from the Etherscan.

We tracked 133,000 Ethereum names and exposed their secrets

The majority of labels fell into three richest ethereum addresses exchanges, miners, and ICO wallets. Clustering is an unsupervised machine learning technique, so I could not use labels to train my model. Instead, I used them to assign user archetypes to clusters, based on the highest label density for richest ethereum addresses cluster.

Results can be found here.

Categorizing addresses using patterns in transaction activity

richest ethereum addresses Known addresses on the left. Re-clustering Exchange and miner addresses were mixed together in the richest ethereum addresses cluster at first.

To separate them, I performed a second round of clustering, using only richest ethereum addresses addresses in that cluster. By changing richest ethereum addresses dissimilarity measure from euclidean distance to cosine distance, I dramatically improved separation between exchanges and miners.

Improved separation of richest ethereum addresses and miners. By substituting results from re-clustering into the original analysis, we end up with 9 richest ethereum engineer bitcoin address. Interpreting the Results We can draw conclusions about user behavior based on the corresponding cluster centroids.

Richest ethereum addresses

Radar plot — cluster centroid address features Exchanges High incoming and outgoing transaction volume Highly irregular time richest ethereum addresses transactions Exchanges are the banks of the crypto space.

These results are intuitive.

Tether has blacklisted 100 addresses on Ethereum, network data shows

Miners Small average transaction size More regular time between transactions Miners secure the blockchain richest ethereum addresses expending computational power, and are rewarded with ether.

It makes sense read more these startups would richest ethereum addresses large war chests, and periodically sell large amounts to cover regular business expenses.

Richest ethereum addresses

richest ethereum addresses Other categories The Exchange and Mining clusters are highly similar, as they were created in the second round of clustering.

Addresses in cluster 7 have a large amount of smart contract activity. Clusters 2 and 5 are highly distinct.

Richest ethereum addresses

Can you identify any of these user groups? Next Steps Expanding on this work would allow a more nuanced view of Ethereum blockchain data.

Richest ethereum addresses

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