Invited Speaker---Assoc. Prof. Xiaoping Zhou


Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Biography: Xiaoping Zhou is an Associate Professor at the Beijing University of Civil Engineering & Architecture. His research interests include big data mining, artificial intelligence and building information model. He has published more than 50 papers in journals and conference proceedings in these research areas (e.g., IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks and Learning Systems, AAAI). He has hosted and completed more than 10 research projects from the National Natural Science Foundation of China, the Beijing Natural Science Foundation, etc.

Speech Title: User Identification across Social Networks: A Network Structure Perspective
Abstract: Identification of anonymous identical users of cross-platforms refers to the recognition of the accounts belonging to the same individual among multiple Social Network (SN) platforms. Evidently, cross-platform exploration may help solve many problems in social computing, in both theory and practice. However, it is still an intractable problem due to the fragmentation, inconsistency and disruption of the accessible information among SNs. Different from the efforts implemented on user profiles and users’ content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions, requiring prior knowledge of some given identified users, are supervised or semi-supervised. It is laborious to label the prior knowledge manually in some scenarios where prior knowledge is hard to obtain. Noticing that friend relationships are reliable and consistent in different SNs, we proposed an unsupervised scheme to address the user identification issue. Undoubtedly, the unsupervised solutions can additionally be utilized to generate prior knowledge for supervised and semi-supervised schemes. In applications, the unsupervised anonymous identical user identification method accommodates more scenarios where the seed users are unobtainable.