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.

矿工在投入大量电力成本的情况下，在工作证明区块链中开采已成为一种昂贵的事务。为了最大化挖掘收益，矿工需要在节点之间选择网络连接，以减少延迟并提高挖矿奖励。本研究通过使用基于网络坐标的模型解决这一问题，并在实验中证明了该算法在不同网络设置下优于或与其他基准算法相当。