  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 the dynamics of the transaction graph impacts the 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, the new graph associated topological features computed using the
tools of persistent homology, are found to exhibit a high utility for
predicting Bitcoin price dynamics. %explain higher order interactions among the
nodes in Blockchain graphs and can be used to build much more accurate price
prediction models. Using the proposed persistent homology-based techniques, we
offer a new elegant, easily extendable and computationally light approach for
graph representation learning on Blockchain.
