ChainNet: Learning on Blockchain Graphs with Topological Features With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction. Unlike other financial networks, such as stock and currency trading, blockchain based cryptocurrencies have the entire transaction graph accessible to the public (i.e., all transactions can be downloaded and analyzed). A natural question is then to ask whether dynamics of the transaction graph impacts price of the underlying cryptocurrency. We show that standard graph features such as degree distribution of the transaction graph may not be sufficient to capture network dynamics and its potential impact on fluctuations of Bitcoin price. In contrast, topological features computed from the blockchain graph using the tools of persistent homology, are found to exhibit higher utility for predicting Bitcoin price dynamics.

Prediction of Bitcoin Transactions Included in the Next Block This paper proposes a method to predict transactions that are likely to be included in the next block from the mempool of unconfirmed transactions in the Bitcoin network. To implement the proposed method, we applied machine learning to the transactions data collected from the Bitcoin network and divided our implementation into the following three objects: Data Collector; Data Preprocessor; and Analyzer. We used the random forest classifier algorithm because the problem of predicting the likelihood of a transaction to be included in the next block is a binary classification problem. We evaluated the performance of our model by comparing transactions in the mempool against transaction published in the next two blocks mined at the time of our experiments. For both blocks, our model has a prediction accuracy of more than 80% and a minimal false negative error. The analysis of transaction inclusion in the next block is fundamental as it could drive the price of Bitcoin or signify the properties of a given transaction such as illegal or legal.