DeepLearn 2023 Winter
8th International School
on Deep Learning
Bournemouth, UK · January 16-20, 2023
Registration
Downloads
  • Call DeepLearn 2023 Winter
  • Poster DeepLearn 2023 Winter
  • Lecture Materials
  • Home
  • Schedule
  • Lecturers
  • News
  • Accommodation
  • Info
    • Travel from London to Bournemouth
    • Sponsoring
    • Code of conduct
    • Visa
    • Testimonials
  • Home
  • Schedule
  • Lecturers
  • News
  • Accommodation
  • Info
    • Travel from London to Bournemouth
    • Sponsoring
    • Code of conduct
    • Visa
    • Testimonials
deeplearn-speaker-nitesh-chawla

Nitesh Chawla

University of Notre Dame

[introductory/intermediate] Graph Representation Learning

Summary

Complex systems, such as web, information, or knowledge systems, are generally represented as (heterogeneous) networks, which are rich in their representation of the underlying characteristics and phenomena of the complex system. These representations include relationships, attributes, content, and temporal information. As such the networks are inherently heterogeneous (or multi-modal), presenting a significantly large exploratory space for manual feature construction for several downstream modeling tasks. This has led to development and popularization of representation learning algorithms for graphs / networks. In this tutorial, we will answer the following questions: What is representation learning on graphs? Why do we need it? How do we do it? How do we tackle the challenges of multiple data modalities? What are some of the applications?

Syllabus

  • Complex Systems as Graphs / Networks
  • Overview of Representation Learning
  • Learning node-based Embeddings: Homogeneous and Heterogeneous Methods
  • Graph Neural Networks

References

1) Cui, P., Wang, X., Pei, J., & Zhu, W. (2018). A survey on network embedding. IEEE Transactions on Knowledge and Data Engineering, 31(5), 833-852.

2) Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584.

3) Liu, X., & Tang, J. (2021). Network representation learning: A macro and micro view. AI Open, 2, 43-64.

4) Dong, Y., Chawla, N. V., & Swami, A. (2017, August). metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 135-144).

5) Zhang, C., Song, D., Huang, C., Swami, A., & Chawla, N. V. (2019, July). Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 793-803).

6) Saebi, M., Ciampaglia, G. L., Kaplan, L. M., & Chawla, N. V. (2020). HONEM: learning embedding for higher order networks. Big Data, 8(4), 255-269.

Pre-requisites

Introductory machine learning, specifically learning on graphs, and network science.

Short bio

Nitesh Chawla is the Frank M. Freimann Professor of Computer Science and Engineering, and Founding Director of the Lucy Family Institute for Data and Society. His research is focused on machine learning, data science, and network science, and is motivated by the question of how technology can advance the common good through interdisciplinary research. He is the recipient of the IEEE CIS Outstanding Early Career Award; the IBM Watson Faculty Award, the IBM Big Data, and Analytics Faculty Award, National Academy of Engineering New Faculty Fellowship, and 1st Source Bank Technology Commercialization Award. In recognition of the societal and community impact of his research, he received the Rodney F Ganey Award and Michiana 40 under 40 honor. He is founder of Aunalytics, a data science software and cloud computing company.

Other Courses

deeplearn-speaker-yi-maYi Ma
Daphna WeinshallDaphna Weinshall
Eric P. XingEric P. Xing
Matias Carrasco KindMatias Carrasco Kind
Sumit ChopraSumit Chopra
Luc De RaedtLuc De Raedt
Marco DuarteMarco Duarte
Joao GamaJoão Gama
Claus HornClaus Horn
Zhiting Hu & Eric P. XingZhiting Hu & Eric P. Xing
deeplearn-speaker-nathalie-japkowiczNathalie Japkowicz
deeplearn-speaker-gregor-kasieczkaGregor Kasieczka
Karen LivescuKaren Livescu
deeplearn-speaker-david-mcallersterDavid McAllester
deeplearn-speaker-dhabaleswar-k-pandaDhabaleswar K. Panda
Fabio RoliFabio Roli
Bracha ShapiraBracha Shapira
deeplearn-speaker-kunal-tawarKunal Talwar
Tinne TuytelaarsTinne Tuytelaars
deeplearn-speaker-lyle-ungarLyle Ungar
speakers-bram-van-ginnekenBram van Ginneken
deeplearn-speaker-yudong-zhangYu-Dong Zhang

DeepLearn 2022 Winter

CO-ORGANIZERS

Bournemouth University
Department of Computing and Informatics

Universitat Rovira i Virgili, Tarragona

Institute for Research Development, Training and Advice – IRDTA, Brussels/London

Active links
  • DeepLearn 2023 Summer – 10th International Gran Canaria School on Deep Learning
  • BigDat 2023 Summer – 7th International School on Big Data
  • DeepLearn 2023 Spring – 9th International School on Deep Learning
Past links
  • DeepLearn 2022 Autumn
  • DeepLearn 2022 Summer
  • DeepLearn 2022 Spring
  • DeepLearn 2021 Summer
  • DeepLearn 2019
  • DeepLearn 2018
  • DeepLearn 2017
© IRDTA 2022. All Rights Reserved.
We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept All”, you consent to the use of ALL the cookies. However, you may visit "Cookie Settings" to provide a controlled consent.
Cookie SettingsAccept All
Manage consent

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
CookieDurationDescription
cookielawinfo-checkbox-advertisement1 yearThe cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Advertisement".
cookielawinfo-checkbox-analytics11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional11 monthsThe cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
PHPSESSIDsessionThis cookie is native to PHP applications. The cookie is used to store and identify a users' unique session ID for the purpose of managing user session on the website. The cookie is a session cookies and is deleted when all the browser windows are closed.
viewed_cookie_policy11 monthsThe cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytics
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
CookieDurationDescription
_ga2 yearsThis cookie is installed by Google Analytics. The cookie is used to calculate visitor, session, campaign data and keep track of site usage for the site's analytics report. The cookies store information anonymously and assign a randomly generated number to identify unique visitors.
_gat_gtag_UA_74880351_91 minuteThis cookie is set by Google and is used to distinguish users.
_gid1 dayThis cookie is installed by Google Analytics. The cookie is used to store information of how visitors use a website and helps in creating an analytics report of how the website is doing. The data collected including the number visitors, the source where they have come from, and the pages visted in an anonymous form.
Advertisement
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
Others
Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.
SAVE & ACCEPT
Powered by CookieYes Logo