DLG-AAAI’21: The Fifth International Workshop on Deep Learning on Graphs: Methods and Applications (DLG-AAAI’21) Fully Virtual Fully Virtual, NY, United States, February 2-9, 2021 |
Conference website | http://deep-learning-graphs.bitbucket.io/dlg-aaai21/ |
Submission link | https://easychair.org/conferences/?conf=dlgaaai21 |
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,
- Modeling highly structured data with time-evolving, multi-relational, and multi-modal nature. Such challenges are profound in applications in social attributed networks, natural language processing, inductive logic programming, and program synthesis and analysis. Joint modeling of text or image content with underlying network structure is a critical topic for these domains.
- Modeling complex data that involves mapping between graph-based inputs and other highly structured output data such as sequences, trees, and relational data with missing values. Natural Language Generation tasks such as SQL-to-Text and Text-to-AMR are emblematic of such challenge.
This one-day workshop aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to the above challenges. The workshop will consist of contributed talks, contributed posters, and invited talks on a wide variety of 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.
Submission 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.
Submission link: https://deep-learning-graphs.bitbucket.io/dlg-aaai21/cfp.html
List of Topics
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.
- Graph neural networks on node-level, graph-level embedding
- Graph neural networks on graph matching
- Dynamic/incremental graph embedding 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
And with particular focuses but not limited to these application domains:
- Learning and reasoning (machine reasoning, theory proving)
- Natural language processing (information extraction, semantic parsing, text generation)
- Bioinformatics (drug discovery, protein generation, protein structure prediction)
- Program synthesis and analysis
- Reinforcement learning (multi-agent learning, compositional imitation learning)
- Financial security (Anti-Money Laundering)
- Cybersecurity (authentication graph, Internet of Things, malware propagation)
- Geographical network modeling and prediction (Transportation and mobility networks, social networks)
Committees
Program Committee
- Ibrahim Abdelaziz, IBM Research AI, USA
- Stephan Günnemann, Technical University of Munich, Germany
- Balaji Ganesan, IBM Research AI, USA
- Tian Gao, IBM Research AI, USA
- William L. Hamilton, McGill University, Canada
- Tengfei Ma, IBM Research AI, USA
- Thomas Kipf, University of Amsterdam, Netherlands
- Yujia Li, DeepMind, UK
- Renjie Liao, University of Toronto, Canada
- Liana Lin (IBM Research AI)
- Yizhou Sun, University of California, Los Angeles, USA
- Qingsong Wen, Alibaba DAMO Academy, USA
- Jie Tang, Tsinghua University, China
- Hanghang Tong, Arizona State University, USA
- Lingfei Wu, IBM Research AI, USA
- Qing Wang (IBM Research AI)
- Yinglong Xia, Facebook AI, USA
- Liang Zhao, George Mason University, USA
- Dawei Zhou, Arizona State University, USA
- Zhen Zhang, Washington University in St. Louis, USA
Organizing committee
- Lingfei Wu (IBM Research AI)
- Jiliang Tang (Michigan State University)
- Yinglong Xia (Facebook AI)
- Jian Pei (Simon Fraser University)
- Dawei Zhou (University of Illinois Urbana-Champaign)
Venue
The conference will be held fully Virtually jointed with AAAI'21, February 2-9, 2021.
Contact
All questions about submissions should be emailed to lwu@email.wm.edu.