tutorial

Multi-modal Network Representation Learning

Authors:

Xiangliang Zhang,

Nitesh V. ChawlaAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 3557 - 3558

https://doi.org/10.1145/3394486.3406475

Published: 20 August 2020 Publication History

Abstract

In today's information and computational society, complex systems are often modeled as multi-modal networks associated with heterogeneous structural relation, unstructured attribute/content, temporal context, or their combinations. The abundant information in multi-modal network requires both a domain understanding and large exploratory search space when doing feature engineering for building customized intelligent solutions in response to different purposes. Therefore, automating the feature discovery through representation learning in multi-modal networks has become essential for many applications. In this tutorial, we systematically review the area of multi-modal network representation learning, including a series of recent methods and applications. These methods will be categorized and introduced in the perspectives of unsupervised, semi-supervised and supervised learning, with corresponding real applications respectively. In the end, we conclude the tutorial and raise open discussions. The authors of this tutorial are active and productive researchers in this area.

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Cited By

Han WCai CGuo YPeng JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)ERL-MR: Harnessing the Power of Euler Feature Representations for Balanced Multi-modal LearningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681215(4591-4600)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681215
Liu YGu RYang ZJi Y(2021)Hierarchical community discovery for multi-stage IP bearer network upgradationJournal of Network and Computer Applications10.1016/j.jnca.2021.103151189:COnline publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.jnca.2021.103151

Index Terms

Multi-modal Network Representation Learning
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Data management systems
    1. Database design and models
      1. Graph-based database models
        Network data models
  2. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

Copyright © 2020 Owner/Author.

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: 20 August 2020

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National Science Foundation

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KDD '20

Sponsor:

KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

July 6 - 10, 2020

CA, Virtual Event, USA

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

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sigkdd
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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Han WCai CGuo YPeng JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)ERL-MR: Harnessing the Power of Euler Feature Representations for Balanced Multi-modal LearningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681215(4591-4600)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681215
Liu YGu RYang ZJi Y(2021)Hierarchical community discovery for multi-stage IP bearer network upgradationJournal of Network and Computer Applications10.1016/j.jnca.2021.103151189:COnline publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.jnca.2021.103151

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