Mining subgraphs from propagation networks through temporal dynamic analysis

Saeid Hosseini, Hongzhi Yin, Meihui Zhang, Yuval Elovici, Xiaofang Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

An alarm is raised due to a defect in a transportation system. Given a graph over which the alarms propagate, we aim to exploit a set of subgraphs with highly correlated nodes (or entities). The edge weight between each pair of entities can be computed using the temporal dynamics of the propagation process. We retrieve the top k edge weights and each group of connected entities can consequently form a tightly coupled subgraph. However, numerous challenges abound. First, the textual contents associated with the alarms of the same type differ during the propagation process. Hence, in the lack of textual data, the temporal information can only be employed to compute the correlation weights. Second, in many scenarios, the same alarm does not propagate. Third, given a pair of entities, the propagation can occur in both directions. Most of the prior work only consider the time-window and assume that the propagation between a pair of entities occurs sequentially. But, the propagation process should be inferred using miscellaneous temporal features. Therefore, we devise a generative approach that, on the one hand, utilizes infinite temporal latent factors (e.g. hour, day, and etc.) to compute the correlation weights, and on the other hand, analyzes how an alarm in one entity can cause a set of alarms in another. We also conduct an extensive set of experiments to compare the performance of the subgraph mining methods. The results show that our unified framework can effectively exploit the tightly coupled subgraphs.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 19th International Conference on Mobile Data Management, MDM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages66-75
Number of pages10
ISBN (Electronic)9781538641330
DOIs
StatePublished - 13 Jul 2018
Externally publishedYes
Event19th IEEE International Conference on Mobile Data Management, MDM 2018 - Aalborg, Denmark
Duration: 26 Jun 201828 Jun 2018

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
Volume2018-June
ISSN (Print)1551-6245

Conference

Conference19th IEEE International Conference on Mobile Data Management, MDM 2018
Country/TerritoryDenmark
CityAalborg
Period26/06/1828/06/18

Keywords

  • Propagation network
  • diffusion network
  • multifaceted temporal properties
  • subgraph mining
  • temporal dynamics

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