Abstract

Networks (i.e., graphs) appears in many high-impact applications. Often these networks are collected from different sources, at different times, at different granularities. In this talk, I will present our recent work on mining such multiple networks. First, we will present several new data models, whose key idea is to leverage networks as context to connect different data sets or different data mining models, including a network of networks (NoN) model, a network of co-evolving time series (NoT) model and a network of regression model. Second, we will present some algorithmic examples on how to perform mining with such new models where the key idea is to leverage the contextual network as an effective regularizer during the mining process, including ranking, imputation, prediction and inference. Finally, we will demonstrate the effectiveness of our new models and algorithms in some applications, including bioinformatics, sensor networks, critical infrastructure networks and scholarly data mining.

Original languageEnglish (US)
Title of host publicationProceeding - 17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
PublisherIEEE Computer Society
Number of pages1
Volume2017-November
ISBN (Electronic)9781538614808
DOIs
StatePublished - Dec 15 2017
Event17th IEEE International Conference on Data Mining Workshops, ICDMW 2017 - New Orleans, United States
Duration: Nov 18 2017Nov 21 2017

Other

Other17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
CountryUnited States
CityNew Orleans
Period11/18/1711/21/17

Keywords

  • Graph Mining
  • Network of Networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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