  In this work, we consider community detection in blockchain networks. We
specifically take the Bitcoin network and Ethereum network as two examples,
where community detection serves in different ways. For the Bitcoin network, we
modify the traditional community detection method and apply it to the
transaction social network to cluster users with similar characteristics. For
the Ethereum network, on the other hand, we define a bipartite social graph
based on the smart contract transactions. A novel community detection algorithm
which is designed for low-rank signals on graph can help find users'
communities based on user-token subscription. Based on these results, two
strategies are devised to deliver on-chain advertisements to those users in the
same community. We implement the proposed algorithms on real data. By adopting
the modified clustering algorithm, the community results in the Bitcoin network
is basically consistent with the ground-truth of betting site community which
has been announced to the public. At the meanwhile, we run the proposed
strategy on real Ethereum data, visualize the results and implement an
advertisement delivery on the Ropsten test net.
