  Mining in proof-of-work blockchains has become an expensive affair requiring
specialized hardware capable of executing several megahashes per second at huge
electricity costs. Miners earn a reward each time they mine a block within the
longest chain, which helps offset their mining costs. It is therefore of
interest to miners to maximize the number of mined blocks in the blockchain and
increase revenue. A key factor affecting mining rewards earned is the
connectivity between miners in the peer-to-peer network. To maximize rewards a
miner must choose its network connections carefully, ensuring existence of
paths to other miners that are on average of a lower latency compared to paths
between other miners. We formulate the problem of deciding whom to connect to
for miners as a combinatorial bandit problem. Each node picks its neighbors
strategically to minimize the latency to reach 90\% of the hash power of the
network relative to the 90-th percentile latency from other nodes. A key
contribution of our work is the use of a network coordinates based model for
learning the network structure within the bandit algorithm. Experimentally we
show our proposed algorithm outperforming or matching baselines on diverse
network settings.
