Multiresolution Mutual Information Method for Social Network Entity Resolution

Abstract

Online Social Networks (OSN) are widely adopted in our daily lives, and it is common for one individual to register with multiple sites for different services. Linking the rich contents of different social network sites is valuable to researchers for understanding human behaviors from different perspectives. For instance, each OSN has its own group of users and thus, has its own biases. Linked accounts can be a good calibration dataset to improve data quality. This Entity Resolution (ER) problem is a challenge in the social network domain that many researchers attempt to tackle. In this paper we take advantage of spatial information posted in different social network sites and propose an efficient multiresolution mutual information approach to link the entities from those sites. The proposed method significantly reduces the computing time by utilizing an iterative coarse-to-fine multiresolution approach, yet is robust in dealing with the sparsity of location data. The human location-wise behavior is also discussed in deciding the resolution level. Public available Twitter and Instagram data collected from their APIs are used to illustrate the method, and the performance is evaluated by comparing it with greedy mutual information approach.