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Efficient Processing of Growing Temporal Graphs

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Database Systems for Advanced Applications (DASFAA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10178))

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Abstract

Temporal graphs are useful in modeling real-world networks. For example, in a phone call network, people may communicate with each other in multiple time periods, which can be modeled as multiple temporal edges. However, the size of real-world temporal graphs keeps increasing rapidly (e.g., considering the number of phone calls recorded each day), which makes it difficult to efficiently store and analyze the complete temporal graphs. We propose a new model, called equal-weight damped time window model, to efficiently manage temporal graphs. In this model, each time window is assigned a unified weight. This model is flexible as it allows users to control the tradeoff between the required storage space and the information loss. It also supports efficient maintenance of the windows as new data come in. We then discuss applications that use the model for analyzing temporal graphs. Our experiments demonstrated that we can handle massive temporal graphs efficiently with limited space.

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Acknowledgements

We thank the reviewers for their valuable comments. The authors are supported by the Hong Kong GRF 2150851 and 2150895, ITF 6904079, MSRA grant 6904224, and CUHK Grants 3132964 and 3132821.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Huanhuan Wu, Yunjian Zhao, James Cheng & Da Yan

Authors
  1. Huanhuan Wu
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  2. Yunjian Zhao
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  3. James Cheng
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  4. Da Yan
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Corresponding author

Correspondence to Huanhuan Wu .

Editor information

Editors and Affiliations

  1. Arizona State University, Tempe - Phoenix, Arizona, USA

    Selçuk Candan

  2. of Science and Technology, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

  3. Aalborg University , Aalborg, Denmark

    Torben Bach Pedersen

  4. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

  5. The University of Queensland , Brisbane, Queensland, Australia

    Wen Hua

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Wu, H., Zhao, Y., Cheng, J., Yan, D. (2017). Efficient Processing of Growing Temporal Graphs. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10178. Springer, Cham. https://doi.org/10.1007/978-3-319-55699-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-55699-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55698-7

  • Online ISBN: 978-3-319-55699-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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