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Characterizing Temporal Bipartite Networks - Sequential- Versus Cross-Tasking

  • Lucas J. J. M. Peters
  • Juan-Juan Cai
  • Huijuan Wang
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)

Abstract

Temporal bipartite networks that describe how users interact with tasks or items over time have recently become available. Such temporal information allows us to explore user behavior in-depth. We propose two metrics, the relative switch frequency and distraction in time to measure a user’s sequential-tasking level, i.e. to what extent a user interacts with a task consecutively without interacting with other tasks in between. We analyze the sequential-tasking level of users in two real-world networks, an user-project and an user-artist network that record users’ contribution to software projects and users’ playing of musics from diverse artists respectively. We find that users in the user-project network tend to be more sequential-tasking than those in the user-artist network, suggesting a major difference in user behavior when subject to work related and hobby-related tasks. Moreover, we investigate the relation (rank correlation) between the two sequential-tasking measures and another 10 nodal features. Users that interact less frequently or more regularly in time (low deviation in the time interval between two interactions) or with fewer items tend to be more sequential-tasking in the user-project network. No strong correlation has been found in the user-artist network, which limits our ability to identify sequential-tasking users from other user features.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lucas J. J. M. Peters
    • 1
  • Juan-Juan Cai
    • 2
  • Huijuan Wang
    • 2
  1. 1.Faculty of Applied SciencesDelft University of TechnologyDelftThe Netherlands
  2. 2.Faculty of Electrical Engineering, Mathematics, and Computer ScienceDelft University of TechnologyDelftThe Netherlands

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