KLCMD: K_Length Clustering for Miner Detection on the blockchain social networks to increase the information propagation speed

Abstract

Information propagation speed on blockchain social networks depends on activation of users on social network and blockchain network as well. Many researches have been done to increase information propagation speed on social networks. Most of these methods consist of finding influential nodes by different methods to propagate information by these nodes. Some researches have been done to increase information propagation speed on blockchain network. These methods consist of modifying validation of blocks, compressing blockchain, removing redundant information, using another consensus algorithm and so on, still, there is no proper method to increase information propagation speed on both of blockchain and social network. In this paper K_Length Clustering for Miner Detection has been proposed for this purpose. It comprises of two clustering and miner detection phases for information propagation on blockchain social networks. In this method a new partition-oriented clustering has been proposed to cluster users of the social network then the suitable user as miner has been chosen for each cluster, in contrast existing methods just use the blockchain network features for miner selection process which it can be unfairly. In this method speed of information propagation has been increased on both networks. This method has been compared with other consensus algorithms and partition-oriented clustering methods. Experimental results show that our approach has improved the speed of information propagation on both of them. Speed improvement in compare of consensus methods is minimum 5.97 % and maximum 6.10 %, in compare of clustering methods is minimum 0.8 % and maximum 46.35 %.