Deep Graph Learning:

Methodologies and Applications

DGLMA'19 Workshop @ IEEE BigData'19

December 9, Los Angeles, CA, USA


In many scientific fields, many important problems can be best expressed with a complex structure, e.g., graph or manifold structure, such as social networks and drug discovery. On one hand, these graph-structured data can encode complicated pairwise relationships for learning more informative representations; On the other hand, the structural and semantic information in sequence data can be exploited to augment original sequence data by incorporating the domain-specific knowledge.

Deep Learning models are at the core of research in Artificial Intelligence nowadays. In recent years, deep learning on graphs has experienced a fast increase in research on these problems, especially for graph representation learning and graph generation. New neural network architectures on graph-structured data have achieved remarkable performance in some well-known domains such as social networks and bioinformatics . They have also infiltrated other fields of science, including computer vision, natural language processing, inductive logic programming, program synthesis and analysis, automated planning, reinforcement learning, financial security, and adversarial machine learning. Despite these successes, graph neural networks (GNNs) still face many challenges when they are used to model highly structured data that are time-evolving, multi-relational, and multi-modal, and the mapping between graphs and other highly structured data such as sequences, trees and graphs. More importantly, new application domains for GNNs that emerge from real world problems introduce significantly more challenges for GNNs.


We invite the submission of novel research paper, demo paper, and visionary papers. Types of submissions are the following:

  1. Long paper (up to 10 pages)
  2. Short paper (up to 6 pages)
  3. Work in progress paper
Submissions must be in PDF format, written in English, and formatted according to the latest double-column IEEE 2-column format. All papers will be peer reviewed, single-blinded. Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. Accepted papers will be published in the IEEE BigData 2019 conference proceedings with the main conference papers. All the papers are required to be submitted here.

For any inquery please email:

We encourage submissions on a broad range of deep learning on graphs. Topics of interest include, but not limited to:

  • Methods
  • - Graph neural networks on node-level, graph-level embedding
  • - Graph neural networks on graph matching
  • - Scalable methods for large graphs
  • - Dynamic/incremental graph-embedding
  • - Learning representation on heterogeneous networks, knowledge graphs
  • - Deep generative models for graph generation/semantic-preserving transformation
  • - Graph2seq, graph2tree, and graph2graph models
  • - Deep reinforcement learning on graphs
  • - Adversarial machine learning on graphs
  • - Spatial and temporal graph prediction and generation
  •    Applications:
  •    - Learning and reasoning (machine reasoning, inductive logic programming, theory proving)
  •    - Natural language processing (information
  • extraction, semantic parsing (AMR, SQL), text generation, machine comprehension)
  •    - Bioinformatics (drug discovery, protein generation, protein structure prediction)
  •    - Program synthesis and analysis
  •    - Automated planning
  •    - Reinforcement learning (multi-agent learning, compositional imitation learning)
  •    - Financial security (Anti-Money Laundering)
  •    - Computer vision (object relation reasoning, graph-based representations for segmentation/tracking)

Note that at least one author from each accepted paper must register to attend the IEEE Big Data 2019 conference and present their work in DGLMA'19.


Oct 1, 2019: Oct 8, 2019         Workshop paper submission due (23:59, Anywhere on Earth)

Nov 1, 2019:       Workshop paper notifications

Nov 15, 2019:      Workshop paper camera-ready deadline (23:59, Anywhere on Earth)

Dec 9-12, 2019:  Workshop Date


Opening & Welcome

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Keynote 1

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Paper Presentation

Invited Talk 1

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Coffee Break

Keynote 2

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Invited Talk 2

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Poster Session

Closing Remarks

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Lingfei Wu IBM Research AI

Dr. Lingfei Wu is a Research Staff Member in the IBM AI Foundations Labs, Ressoning group at IBM T. J. Watson Research Center. He earned his Ph.D. degree in computer science from the College of William and Mary in 2016. Lingfei Wu is a passionate researcher and responsible team leader, developing novel deep learning/machine learning models for solving real-world challenging problems. He has served as the PI in IBM for several federal agencies such as DARPA and NSF (more than $1.8M), as well as MIT-IBM Watson AI Lab. He has published more than 50 top-ranked conference and journal papers in ML/DL/NLP domains and is a co-inventor of more than 20 filed US patents. He was the recipient of the Best Paper Award and Best Student Paper Award of several conferences such as IEEE ICC'19 and KDD workshop on DLG'19. His research has been featured in numerous media outlets, including NatureNews, YahooNews, Venturebeat, TechTalks, SyncedReview, Leiphone, QbitAI, MIT News, IBM Research News, and SIAM News. He has organized or served as Poster co-chairs of IEEE BigData'19, Tutorial co-chairs of IEEE BigData'18, Workshop co-chairs of Deep Learning on Graphs (with KDD'19, IEEE BigData’19, and AAAI'20), and regularly served as a SPC/TPC member of the following major AI/ML/DL/DM/NLP conferences including NIPS, ICML, ICLR, ACL, IJCAI, AAAI, and KDD.


Jiliang Tang Michigan State University

Jiliang Tang is an assistant professor in the computer science and engineering department at Michigan State University. Before that, he was a research scientist in Yahoo Research and got his PhD from Arizona State University in 2015. He focuses on developing learning, mining and optimization algorithms from the graph perspective . He was the recipients of The NSF Career Award, the Best Paper Award in ASONAM 2018, the Best Student Paper Award in WSDM2018, the Best Paper Award in KDD2016, the runner up of the Best KDD Dissertation Award in 2015, Dean's Dissertation Award and the best paper shortlist of WSDM2013. He is now associate editors of ACM TKDD, ICWSM, and Neurocomputing. He has published his research in highly ranked journals and top conference proceedings, which received thousands of citations and extensive media coverage.


Liang Zhao George Mason University

Dr. Liang Zhao is an assistant professor at Information Science and Technology Department at George Mason University. He got his PhD degree from Computer Science Department at Virginia Tech. His research interests include big data mining, artificial intelligence, and machine learning, with particular emphasis on social network modeling, and deep learning on graphs, and spatiotemporal data mining. He got the NSF CRII award in 2018. He got Jeffress Trust Awards in 2019 for supporting his ongoing research on graph generative deep learning. He is named as one of the “Top 20 Data mining Rising Star” by Microsoft Academic Search in 2016. He won the dissertation award of Computer Science Department at Virginia Tech. He has published over 50 papers in top journals and conferences in data mining, machine learning, and artificial intelligence.

Publicity Chair


Tyler Derr Michigan State University

Tyler Derr is a Ph.D. student in the department of Computer Science and Engineering at Michigan State University (MSU). His main research interest is in network analysis, with particular emphasis on signed networks and graph neural networks. He was the recipient of the Best Reviewer Award at ICWSM2019, the Best Student Poster Award at SDM2019, and the "People's Choice" Award for the 3 Minute Thesis Competition at MSU. He has published several papers in these domains at some of the top international conferences and received travel awards to present his work. He actively serves as a reviewer/program committee member for journals/conferences in his research domain. Tyler is also currently on the job market for a tenure-track Assistant Professor position at a research institution in Computer Science.

Program Committee

Tyler Derr
    (Michigan State University)
Amin Javari
    (University of Illinois at Urbana-Champaign)
Chen Lin
    (IBM Research)

Yue Ning
    (Stevens Insitute of Technology)
Qing Wang
    (IBM T. J. Watson Research Center)
Suhang Wang
    (The Pennsylvania State University)

Zhiwei Wang
    (Michigan State University)
Lingfei Wu
    (IBM T. J. Watson Research Center)
Zhen Zhang
    (Washington University in St. Louis)