ONE-TO-MANY NODE MATCHING BETWEEN COMPLEX NETWORKS

Revealing the corresponding identities of the same individual in different systems is a common task in various areas, e.g., criminals inter-network tracking, homologous pro-teins revealing, ancient words translating, and so on. With the reason that, recently, more and more complex systems are described by networks, this task can also be accom-plished by solving a node matching problem among these networks. Revealing one-to-one matching between networks is for sure the best if we can, however, when the target net-works are highly symmetric, or an individual has different identities (corresponds to several nodes) in the same network, the exact one-to-one node matching algorithms always lose their effects to obtain acceptable results. In such situations, one-to-many (or many-to-many) node matching algorithms may be more useful. In this paper, we propose two one-to-many node matching algorithms based on local mapping and ensem-bling, respectively. Although such algorithms may not tell us the exact correspondence of the identities in different systems, they can indeed help us to narrow down the inter-network searching range, and thus are of significance in practical applications. These results have been verified by the matching experiments on pairwise artificial networks and real-world networks