Jiajing Zhang
ABSTRACT
Different social networks are independent of each other, and identifying the same person in different social networks is of great significance for cross-network information dissemination and the construction of a complete portrait of users. Existing methods for user identification via network embedding ignore the different contributions of neighbors and only consider low-order friend relationships. Aiming at these shortcomings, an algorithm of Network Embedding based on Multi-Orders Friends Contribution for User Identification (MOFC) is proposed. Firstly, the difference contribution degree of nodes to their friends is used to assign different weights to the edges between the nodes and their friends. Secondly, the embedding representation vectors of multi-orders friend contribution are used to describe the different orders friend relations of the nodes, and then the representation vectors of different orders are merged to more comprehensively represent network features. Finally, the fusion representation vectors are used as input sample of a neural network to train and build a user identification model. Experiments show that the evaluation indicators of the model have been significantly improved compared with several advanced methods. Compared with the newer and better the CSN_LINE, the accuracy rate of MOFC can be increased by 4.83%, the precision rate can be increased by 6.77%, the recall rate can be increased by 5.49%, the F1-score can be increased by 5.43%.
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Index Terms
- MOFC: Network Embedding Based on Multi-Orders Friends Contribution for User Identification
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