›› 2015, Vol. 30 ›› Issue (5): 1141-1153.doi: 10.1007/s11390-015-1589-z

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Social Media Processing • Previous Articles     Next Articles

Discovering Family Groups in Passenger Social Networks

Huai-Yu Wan1(万怀宇), Member, CCF, Zhi-Wei Wang1(王志伟) You-Fang Lin1(林友芳), Xu-Guang Jia2(贾旭光), Yuan-Wei Zhou2(周元炜)   

  1. 1 Beijing Key Laboratory of Traffic Data Analysis and Mining, School of Computer and Information Technology Beijing Jiaotong University, Beijing 100044, China;
    2 TravelSky Technology Limited, Beijing 100010, China
  • Received:2014-09-16 Revised:2015-06-12 Online:2015-09-05 Published:2015-09-05
  • About author:Huai-Yu Wan received his Ph.D. degree in computer science and technology from Beijing Jiaotong University, Beijing, in 2012. He is an assistant professor with the School of Computer and Information Technology, Beijing Jiaotong University. His research interests focus on data mining, social network analysis, and recommender systems.
  • Supported by:

    This work was partially supported by the Fundamental Research Funds for the Central Universities of China, the National Natural Science Foundation of China under Grant No. 61403023, the Beijing Committee of Science and Technology under Grant No. Z131110002813118, and the Program for Changjiang Scholars and Innovative Research Team in University of China under Grant No. IRT201206.

People usually travel together with others in groups for different purposes, such as family members for visiting relatives, colleagues for business, friends for sightseeing and so on. Especially, the family groups, as a kind of the most common consumer units, have a considerable scale in the field of passenger transportation market. Accurately identifying family groups can help the carriers to provide passengers with personalized travel services and precise product recommendation. This paper studies the problem of finding family groups in the field of civil aviation and proposes a family group detection method based on passenger social networks. First of all, we construct passenger social networks based on their co-travel behaviors extracted from the historical travel records; secondly, we use a collective classification algorithm to classify the social relationships between passengers into family or non-family relationship groups; finally, we employ a weighted community detection algorithm to find family groups, which takes the relationship classification results as the weights of edges. Experimental results on a real dataset of passenger travel records in the field of civil aviation demonstrate that our method can effectively find family groups from historical travel records.

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