The First International Workshop on Deep Learning on Graphs: Methodologies and Applications (DLGMA’20)

February 8, 2020
New York, NY, USA

In Conjunction with the Thirty-Fourth AAAI Conference on Artificial Intelligence
February 7-12, 2020
Hilton New York Midtown
New York, New York USA
AAAI 2020 logo

Best Paper Award

The Best Paper Award goes to Chengxi Zang and Fei Wang for their paper Neural Dynamics on Complex Networks


The Best Student Paper Award goes to Yu Chen, Lingfei Wu and Mohammed J. Zaki for their paper Deep Iterative and Adaptive Learning for Graph Neural Networks


How to attend remotely?

For the people who are affected by this unexpected event of 2019 Novel Coronavirus (2019-nCoV) Outbreak, we are providing the way for everyone to attend remotely. Please use this webex link ( AAAI DLGMA'20 Workshop) for attending the workshop on 02/08/2020!

Workshop Program

Time Title Speakers/Authors
8:50-9:00pm Morning Session: Opening Remarks Lingfei Wu, Charu Aggarwal
9:00-9:30am Keynote Talk 1: Geometric Deep Learning for Functional Protein Design Michael Bronstein, Imperial College London, UK
9:30-10:00am Keynote Talk 2: Representation Learning on Graphs: From Associative Memory to Hyperbolic Geometry Maximilian Nickel, Facebook AI, USA
10:00-10:30pm Poster Spotlight Talks (1 minutes each): total 24 talks
10:30-11:30pm Poser Session I (overlapped with Coffee Break)
11:30-12:00am Keynote Talk 3: Learning to Represent, Optimize, and Generate Molecular Graphs Tommi Jaakkola, Massachusetts Institute of Technology, USA
12:00-12:15pm Contributed Talk 1: Deep Iterative and Adaptive Learning for Graph Neural Networks Yu Chen, Lingfei Wu and Mohammed J. Zaki
12:15-12:30pm Contributed Talk 2: Neural Dynamics on Complex Networks Chengxi Zang and Fei Wang
12:30-13:55pm Lunch Break
13:55-14:00pm Afternoon Session: Opening Remarks Yinglong Xia, Jian Tang
14:00-14:30pm Keynote Talk 4: Reasoning in Knowledge Graphs using Deep Learning Jure Leskovec, Stanford University, USA
14:30-15:00pm Keynote Talk 5: Robust Logic Reasoning with Graph Neural Networks Le Song, Georgia Institute of Technology, USA
15:00-15:15pm Contributed Talk 3: Neural-Symbolic Reasoning over Knowledge Graph for Multi-Stage Explainable Recommendation Yikun Xian, Zuohui Fu, Qiaoying Huang, S. Muthukrishnan and Yongfeng Zhang
15:15-15:30pm Contributed Talk 4: Graph Neural Ordinary Differential Equations Michael Poli, Stefano Massaroli, Junyoung Park, Atsushi Yamashita, Hajime Asama and Jinkyoo Park
15:30-16:30pm Poser Session II (overlapped with Coffee Break)
16:30-17:00pm Keynote Talk 6: Meta Learning and Logical Induction on Graphs Will Hamilton, McGill University, CA
17:00-17:30pm Keynote Talk 7: Deep Graph Library: Overview, Updates, and Future Directions George Karypis, University of Minnesota and Amazon AWS, USA
17:30-17:45pm Best Paper Ceremony and Concluding Remarks Lingfei Wu, Jian Tang, Yinglong Xia, Charu Aggarwal

Accepted Papers

Call for Papers

Deep Learning models are at the core of research in Artificial Intelligence research today. It is well-known that deep learning techniques that were disruptive for Euclidean data such as images or sequence data such as text are not immediately applicable to graph-structured data. This gap has driven a tide in research for deep learning on graphs on various tasks such as graph representation learning, graph generation, and graph classification. New neural network architectures on graph-structured data have achieved remarkable performance in these tasks when applied to domains such as social networks, bioinformatics and medical informatics.

This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including computer vision, natural language processing, inductive logic programming, program synthesis and analysis, automated planning, reinforcement learning, and financial security. Despite these successes, graph neural networks (GNNs) still face many challenges namely,

This one-day workshop aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to above challenges. The workshop will consist of contributed talks, contributed posters, and invited talks on a wide variety of the methods and applications. Work-in-progress papers, demos, and visionary papers are also welcome. This workshop intends to share visions of investigating new approaches and methods at the intersection of Graph Neural Networks and real-world applications.

Topic of interest (including but not limited to)

We invite submission of papers describing innovative research and applications around the following topics. Papers that introduce new theoretical concepts or methods, help to develop a better understanding of new emerging concepts through extensive experiments, or demonstrate a novel application of these methods to a domain are encouraged.

And with particular focuses but not limited to these application domains:

Awards and Sponsors

  • Best Paper Awards: the program committee will nominate a paper for the Best Paper Award and a paper for the Best Student Paper Award. The Best (Student) Paper Awards will include a cash prize. Stay tuned for this year!
  • Travel Awards: students with accepted papers have a chance to apply for a travel award (up to $500).
  • Sponsor Agreement: Word Version, PDF Version
  • Silver Sponsors: Databricks
  • Bronze Sponsors: Ehealth and Facebook

Important Dates

Paper Guidelines

Submissions are limited to a total of 5 pages for initial submission (up to 6 pages for final camera-ready submission), excluding references or supplementary materials, and authors should only rely on the supplementary material to include minor details that do not fit in the 5 pages. All submissions must be in PDF format and formatted according to the new Standard AAAI Conference Proceedings Template. Following this AAAI conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity of writing. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. Authors are strongly encouraged to make data and code publicly available whenever possible. The accepted papers will be posted on the workshop website and will not appear in the AAAI proceedings. Special issues in flagship academic journals are under consideration to host the extended versions of best/selected papers in the workshop.

All submissions must be uploaded electronically at

Contact information:

Keynote Speakers

Workshop Co-Chairs

Sponsorship, Media, and Publicity Co-Chairs

Technical Program Committee