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Recent Developments of Deep Heterogeneous Information Network Analysis

Published: 03 November 2019 Publication History

Abstract

Recently, there is a surge of research on employing Heterogeneous Information Networks (HIN) to model complex interaction system, where networks compose of different types of nodes or links, since HIN contains richer structure and semantic information. Many researches develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Furthermore, recent advancement on deep learning and network embedding poses new opportunities and challenges to mine HIN, and heterogeneous network embedding, even heterogeneous graph neural network, is becoming a hot topic. In this tutorial, we will give a survey on recent developments of heterogeneous information network analysis, especially on newly emerging heterogeneous network embedding. This tutorial shall help researchers and practitioners to share new techniques for identifying and analyzing relationships in networks that integrate multiple types or sources of information.

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  • (2023)Investigating Trace Equivalences in Information NetworksElectronics10.3390/electronics1204086512:4(865)Online publication date: 8-Feb-2023
  • (2023)Incorporating metapath interaction on heterogeneous information network for social recommendationFrontiers of Computer Science10.1007/s11704-022-2438-118:1Online publication date: 12-Aug-2023

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cover image ACM Conferences
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
November 2019
3373 pages
ISBN:9781450369763
DOI:10.1145/3357384
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2019

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Author Tags

  1. data mining
  2. graph mining
  3. heterogeneous information network

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CIKM '19
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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all
  • (2023)Investigating Trace Equivalences in Information NetworksElectronics10.3390/electronics1204086512:4(865)Online publication date: 8-Feb-2023
  • (2023)Incorporating metapath interaction on heterogeneous information network for social recommendationFrontiers of Computer Science10.1007/s11704-022-2438-118:1Online publication date: 12-Aug-2023

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