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Learning From Networks: Algorithms, Theory, and Applications

Published: 25 July 2019 Publication History

Abstract

Arguably, every entity in this universe is networked in one wayr another. With the prevalence of network data collected, such as social media and biological networks, learning from networks has become an essential task in many applications. It is well recognized that network data is intricate and large-scale, and analytic tasks on network data become more and more sophisticated. In this tutorial, we systematically review the area of learning from networks, including algorithms, theoretical analysis, and illustrative applications. Starting with a quick recollection of the exciting history of the area, we formulate the core technical problems. Then, we introduce the fundamental approaches, that is, the feature selection based approaches and the network embedding based approaches. Next, we extend our discussion to attributed networks, which are popular in practice. Last, we cover the latest hot topic, graph neural based approaches. For each group of approaches, we also survey the associated theoretical analysis and real-world application examples. Our tutorial also inspires a series of open problems and challenges that may lead to future breakthroughs. The authors are productive and seasoned researchers active in this area who represent a nice combination of academia and industry.

Supplementary Material

Part 1 of 4 (p3221-huang-part1.mp4)
Part 2 of 4 (p3221-huang-part2.mp4)
Part 3 of 4 (p3221-huang-part3.mp4)
Part 4 of 4 (p3221-huang-part4.mp4)

Cited By

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  • (2021)Deep graph similarity learning: a surveyData Mining and Knowledge Discovery10.1007/s10618-020-00733-535:3(688-725)Online publication date: 24-Mar-2021

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Published In

cover image ACM Conferences
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
July 2019
3305 pages
ISBN:9781450362016
DOI:10.1145/3292500
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: 25 July 2019

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Author Tags

  1. graph analysis
  2. graph neural network
  3. network embedding

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  • Tutorial

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KDD '19
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KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

View all
  • (2021)Deep graph similarity learning: a surveyData Mining and Knowledge Discovery10.1007/s10618-020-00733-535:3(688-725)Online publication date: 24-Mar-2021

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