Time	Title	Speakers/Authors
8:00-8:15pm	Morning Session: Opening Remarks	Jian Pei, Lingfei Wu, Tim Weninger
8:15-8:45am	Keynote Talk 1: Graph Structure of Neural Networks: Good Neural Networks Are Alike [Slides][Video]	Jure Leskovec, Stanford University, USA
8:45-9:15am	Keynote Talk 2: Broad Learning Via Heterogenous Information Networks [Slides][Video]	Philip S. Yu, University of Illinois at Chicago, USA
09:15-09:45pm	Parallel Contributed Talks -- DLG Track Talk 1: Learning Attribute-Structure Co-Evolutions in Dynamic Graphs [Slides][Video] Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang Talk 2: Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling [Slides][Video] Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio, Veronika Thost, Lingfei Wu, Kenneth Forbus and Achille Fokoue
09:15-09:45pm	Parallel Contributed Talks -- MLG Track Talk 1: Understanding and Evaluating Structural Node Embeddings [Slides][Video] Junchen Jin, et al. Talk 2: Mining Persistent Activity in Continually Evolving Networks [Slides][Video] Caleb Belth, et al.
09:45-10:15am	Keynote Talk 3: Deep Graph Mining for Healthcare [Slides][Video]	Fei Wang, Cornell University, USA
10:15-10:30pm	Coffee Break/Social Networking
10:30-11:00am	Keynote Talk 4: The Power of Summarization in Network Analysis [Slides][Video]	Danai Koutra, University of Michigan, USA
11:00-11:30am	Keynote Talk 5: Algorithmic Inductive Biases [Slides][Video]	Petar Veličković, Deepmind, UK
11:30-12:00pm	Parallel Poster Session (Spotlight Talks + LiveQA ) Breakout Z-rooms for both DLG and MLG
12:00-13:00pm	Lunch Break
13:00-13:30am	Keynote Talk 6: Deep Learning for Drug Development [Slides][Video]	Jimeng Sun, University of Illinois Urbana-Champaign, USA
13:30-14:00pm	Parallel Contributed Talks -- DLG Track Talk 3: Demystifying Graph Neural Networks with Graph FilterAssessment [Slides][Video] Yewen Wang, Ziniu Hu, Yusong Ye and Yizhou Sun Talk 4: Early Fraud Detection with Augmented Graph Learning [Slides][Video] Tong Zhao*, Bo Ni*, Wenhao Yu, Meng Jiang
13:30-14:00pm	Parallel Contributed Talks -- MLG Track Talk 3: BRGAN: Generating Graphs of Bounded Rank [Slides][Video] William Shiao, et al. Talk 4: Heterogeneous Threshold Estimation for Linear Threshold Modeling [Slides][Video] Christopher Tran, et al.
14:00-14:30am	Keynote Talk 7: Self-supervised Learning on Graphs: Deep Insights and New Directions [Slides][Video]	Tyler Derr, Vanderbilt University, USA
14:30-14:45pm	Coffee Break/Social Networking
14:45-15:15am	Keynote Talk 8: Learning Graph Strcuture Features for Inductive Link Prediction and Matrix Completion [Slides][Video]	Muhan Zhang, Facebook AI, USA
15:15-15:45am	Keynote Talk 9: Cybersecurity with Graph Neural Networks [Slides][Video]	Le Song, Georgia Institute of Technology, USA
15:45-16:00am	Best Paper Award Ceremony + Final Remarks [Video]	Jian Pei, Lingfei Wu, Yinglong Xia, Hongxia Yang, Jiezhong Qiu
16:00-17:00pm	Parallel Poster Session (Spotlight Talks + LiveQA ) Breakout Z-rooms for both DLG and MLG

• [Poster Spotlight Talk] Non-IID Graph Neural Networks, Yiqi Wang, Yao Ma, Charu Aggarwal and Jiliang Tang
• [Poster Spotlight Talk]Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling,Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio, Veronika Thost, Lingfei Wu, Kenneth Forbus and Achille Fokoue
• [Poster Spotlight Talk] Robust Network Enhancement from Flawed Networks , Jiarong Xu, Yang Yang, Chunping Wang, Zongtao Liu, Jing Zhang, Lei Chen and Jiangang Lu
• [Poster Spotlight Talk] Demystifying Graph Neural Networks with Graph Filter Assessment , Yewen Wang, Ziniu Hu, Yusong Ye and Yizhou Sun
• [Poster Spotlight Talk]Graph Neural Networks with Extreme Nodes Discrimination , George Panagopoulos and Hamid Jalalzai
• [Poster Spotlight Talk] Learning Distributed Representations of Graphs with Geo2DR , Paul Scherer and Pietro Lio
• [Poster Spotlight Talk] PanRep: Universal node embeddings for heterogeneous graphs , Vassilis Ioannidis, Da Zheng and George Karypis
• [Poster Spotlight Talk] Learning Attribute-Structure Co-Evolutions in Dynamic Graphs , Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. Chawla and Meng Jiang
• [Poster Spotlight Talk] Early Fraud Detection with Augmented Graph Learning , Tong Zhao, Bo Ni, Wenhao Yu and Meng Jiang
• [Poster Spotlight Talk] GNN-based Graph Anomaly Detection with Graph Anomaly Loss , Tong Zhao, Chuchen Deng, Kaifeng Yu, Tianwen Jiang, Daheng Wang and Meng Jiang
• [Poster Spotlight Talk] Time-aware GCN: Representation Learning for Mobile App Usage Time-series Data , Kohsuke Kubota and Keiichi Ochiai
• [Poster Spotlight Talk] Hierarchical Attention Models for Multi-Relational Graphs , Roshni Iyer, Wei Wang and Yizhou Sun
• [Poster Spotlight Talk] Heterogeneous Mini-Graph Neural Network and Its Application to Fraud Invitation Detection , Yong-Nan Zhu, Xiaotian Luo, Yu-Feng Li, Bin Bu, Kaibo Zhou, Wenbin Zhang and Mingfan Lu
• [Poster Spotlight Talk] UniKER: A Unified Framework for Combining Embedding and Horn Rules for Knowledge Graph Inference , Kewei Cheng, Ziqing Yang, Ming Zhang and Yizhou Sun
• [Poster Spotlight Talk] Knowledge Graph Embedding using Graph Convolutional Networks with Relation-Aware Attention, Nasrullah Sheikh, Xiao Qin, Berthold Reinwald, Christoph Miksovic, Thomas Gschwind and Paolo Scotton
• [Poster Spotlight Talk] NoiGAN: Noise Aware Knowledge Graph Embedding with Adversarial Learning , Kewei Cheng, Yikai Zhu, Ming Zhang and Yizhou Sun
• [Poster Spotlight Talk] Scalable Dynamic Graph Representation Learning via Incremental Graph Embedding , M. Clara De Paolis Kaluza, Lingfei Wu, Veronika Thost, Ibrahim Abdelaziz and Achille Fokoue

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 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.

• Graph neural networks on node-level, graph-level embedding
• Joint learning of graph neural networks and graph structure
• Graph neural networks on graph matching
• 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

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

• Learning and reasoning (machine reasoning, inductive logic programming, 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)

• Paper submission: June 15, 2020
• Author notification: July 15, 2020
• Camera-Ready: August 1, 2020
• Workshop date: August 24th, 2020

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 KDD Conference Proceedings Template. Following this KDD 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 KDD 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 https://easychair.org/conferences/?conf=dlg2020

This year DLG will be held jointly with The International Workshop on Mining and Learning with Graphs (MLG-KDD'20). DLG and MLG will maintain a separate submission website and program committee.

Contact information: lwu@email.wm.edu

• Jure Leskovec, Stanford University, USA (confirmed))
• Fei Wang, Cornell University, USA (Confirmed)
• Jiliang Tang, Michigan State University, USA (Confirmed)
• Jian Tang, Mila-Quebec AI Institute, Canada (Confirmed)
• Le Song, Georgia Institute of Technology, USA (Confirmed)
• Philip S. Yu, University of Illinons at Chicago, USA (Confirmed)
• Jimeng Sun, UIUC, USA (Confirmed)
• Petar Veličković, Deepmind, UK (Confirmed)
• Muhan Zhang, Facebook AI, USA (Confirmed)

• Lingfei Wu (IBM Research AI, USA)
• Yinglong Xia (Facebook AI, USA)
• Hongxia Yang (Alibaba, China)
• Jiezhong Qiu (Tsinghua University, China)

• Charu Aggarwal, IBM Research AI, USA
• Jian Pei, Simon Fraser University, Canada
• Jie Tang, Tsinghua University, China
• Michalis Vazirgiannis, École Polytechnique, France
• Philip S. Yu, University of Illinois at Chicago, USA
• Xuemin Lin, University of New South Wales, Australia
• Jiebo Luo, University of Rochester, USA
• Lingfei Wu, IBM Research AI, USA
• Yinglong Xia, Facebook AI, USA
• Yuxiao Dong, Microsoft Research, USA
• Hongxia Yang, Alibaba, China
• Jiliang Tang, Michigan State University, USA
• William L. Hamilton, McGill University, Canada
• Thomas Kipf, University of Amsterdam, Netherlands

• 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
• Renjie Liao, University of Toronto, Canada
• Chen (Liana) Lin, IBM Research AI, USA
• Qingsong Wen, Alibaba DAMO Academy, USA
• Qing Wang, IBM Research AI, USA
• Xiaojie Guo, George Mason University, USA
• Yuyang Gao, George Mason University, USA
• Dawei Zhou, Arizona State University, USA
• Zhen Zhang, Washington University in St. Louis, USA
• Yu Chen, Rensselaer Polytechnic Institute, USA
• Xinyi Zhang, Facebook AI, USA
• Shen Wang, University of Illinois at Chicago, USA
• Lingwei Chen, Penn State University
• Clara Paolis, Northeastern University, USA
• Tyler Derr, Michigan State University, USA
• Yao Ma, Michigan State University, USA
• Yunsheng Bai, University of California, Los Angeles, USA
• Liping Liu, Tufts University, USA
• Chuan Lei, IBM Reseach AI, USA

• DLG-AAAI'20
• DGLMA-BigData'19
• DLG-KDD'19