NSGA2-based Multi-label Seed Node Selection in Network Environments
LI Lei① CHU Yuqi① WANG Meng① HAN Li② WU Xindong①③
①(School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China) ②(Basic Research Management Center, Ministry of Science and Technology, Beijing 100862, China) ③(School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette 70503, USA)
Abstract：With the expanding scale of social networks, the label classification of nodes in the network is no longer single but various, which prompts the multi-label classification in social networks to become an important research area. The previous research focuses on how to improve the precision of the predicted labels, while ignoring the system overhead caused by obtaining the node information, such as time consumption and computing memory occupancy. Now, as both expansion and complexity of the networks are increasing, the problem of previously neglected system overhead is becoming the more and the more serious. It increases not only the cost but also the difficulty of predicting labels. In this paper, an NSGA2-based multi-label seed selection algorithm in network environments (NAMESEA) is proposed to improve the accuracy of label prediction on the condition that reducing both the time consume and the memory occupancy. Compared with other multi-label prediction algorithms on multiple real datasets, NAMES
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