CatchTartan: Representing and summarizing dynamic multicontextual behaviors

Meng Jiang, Christos Faloutsos, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Representing and summarizing human behaviors with rich contexts facilitates behavioral sciences and user-oriented services. Traditional behavioral modeling represents a behavior as a tuple in which each element is one contextual factor of one type, and the tensorbased summaries look for high-order dense blocks by clustering the values (including timestamps) in each dimension. However, the human behaviors are multicontextual and dynamic: (1) each behavior takes place within multiple contexts in a few dimensions, which requires the representation to enable non-value and set-values for each dimension; (2) many behavior collections, such as tweets or papers, evolve over time. In this paper, we represent the behavioral data as a two-level matrix (temporal-behaviors by dimensional values) and propose a novel representation for behavioral summary called Tartan that includes a set of dimensions, the values in each dimension, a list of consecutive time slices and the behaviors in each slice. We further develop a propagation method CATCHTARTAN to catch the dynamic multicontextual patterns from the temporal multidimensional data in a principled and scalable way: it determines the meaningfulness of updating every element in the Tartan by minimizing the encoding cost in a compression manner. CATCHTARTAN outperforms the baselines on both the accuracy and speed. We apply CATCHTARTAN to four Twitter datasets up to 10 million tweets and the DBLP data, providing comprehensive summaries for the events, human life and scientific development.

Original languageEnglish (US)
Title of host publicationKDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages945-954
Number of pages10
ISBN (Electronic)9781450342322
DOIs
StatePublished - Aug 13 2016
Event22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States
Duration: Aug 13 2016Aug 17 2016

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume13-17-August-2016

Other

Other22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
CountryUnited States
CitySan Francisco
Period8/13/168/17/16

Keywords

  • Behavior representation
  • Behavior summarization
  • Minimum description length

ASJC Scopus subject areas

  • Software
  • Information Systems

Fingerprint Dive into the research topics of 'CatchTartan: Representing and summarizing dynamic multicontextual behaviors'. Together they form a unique fingerprint.

  • Cite this

    Jiang, M., Faloutsos, C., & Han, J. (2016). CatchTartan: Representing and summarizing dynamic multicontextual behaviors. In KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 945-954). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 13-17-August-2016). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939749