SDM'19 Workshop on

Deep Learning for Graph

Introduction


Graphs (a.k.a., networks) are the universal data structures for representing the relationships between interconnected objects. They are ubiquitous in a variety of disciplines and domains ranging from computer science, social science, economics, medicine, to bioinformatics. Representative examples of real-world graphs include social networks, knowledge graph, protein-protein interaction graphs, and molecular structures. Graph analysis techniques can be used for a variety of applications such as recommending friends to users in a social network, predicting the roles of proteins in a protein-protein interaction network, and predicting the properties of molecule structures for discovering new drugs.

One of the most fundamental challenges of analyzing graphs is effectively representing graphs, which largely determines the performance of many follow-up tasks. This workshop aims to discuss the latest progress on graph representation learning and their applications in different fields. We aim to bring researchers from different communities such as machine learning, network science, natural language understanding, recommender systems, drug discovery. We specially invite submissions related to toolkits and frameworks which make it easy to apply deep learning on graphs. The topics of interest include but are not limited to:

  • Unsupervised node representation learning
  • Learning representations of entire graphs
  • Graph neural networks
  • Graph generation
  • Adversarial attacks to graph representation methods
  • Heterogeneous graph embedding
  • Knowledge graph embedding
  • Graph alignment
  • Dynamic graph representation
  • Graph matching
  • Graph representation learning for relational reasoning
  • Graph anomaly detection
  • Applications in recommender systems
  • Applications in natural language understanding
  • Applications in drug discovery
  • Toolkits and frameworks which make it easy to apply deep learning on graphs
  • Other applications

Target Audience


This workshop could be potentially interesting to researchers in a variety of fields including researchers working on fundamental research of representation learning (especially graph representation learning), and researchers in different application domains of graph representation learning including network science, recommender systems, natural language understanding, and drug discovery.

Program

Following is the tentative agenda for the worksop:


9:00-9:15: Opening Remarks

9:15-9:45: Invited Talk

9:45:10:15: Invited Talk

10:15-10:45: Coffee Break

10:45-11:30 First Poster Session

11:30-12:00: Presentation of selected papers

12:00-2:00: Lunch Break

2:00-2:30: Invited Talk

2:30-3:00: Invited Talk

3:00-3:30: Coffee Break

3:30: 4:00: Invited Talk

4:00 - 4:45 : Panel Discussion on Recent Trends in Deep Learning for Graphs

4:45-5:00: Concluding Remarks

Organizers

  • Jian Tang is currently an assistant professor at Montreal Institute for Learning Algorithms (Mila) and HEC Montreal since December, 2017. He ´ finished his Ph.D. at Peking University in 2014, was a researcher at Microsoft Research between 2014-2016, and was a Postdoc fellow at the University of Michigan and Carnegie Mellon University between 2016- 2017. His research focuses on graph representation learning with applications in natural language understanding, recommender systems, and drug discovery. Most of his papers are published in top-tier venues across machine learning and data mining conferences (ICML, KDD, WWW, and WSDM). He co-organized a tutorial on graph representation learning at KDD 2017, and published one of the first papers on node representation learning (LINE). One of his papers on learning extremely low-dimensional node representations for graph visualization (LargeVis) was nominated for the best paper at WWW 2016. He also received a best paper at ICML 2014 for a constructive theoretical analysis of statistical topic models.
  • Shagun Sodhani is a MSc student at Montreal Institute for Learning Algorithms (Mila) since September 2017 under supervision of Dr Jian Tang. Prior to that, he was working with the Machine Learning team at Adobe Systems where he was awarded the Outstanding Young Engineer Award. His research interest focuses on applications of graph representation learning.
  • William L. Hamilton is a Visiting Researcher at Facebook AI Research, and he will be joining McGill University’s School of Computer Science as an Assistant Professor in January 2019. He completed his PhD at Stanford University in 2018 under the supervision of Jure Leskovec and Dan Jurafsky, and prior to that he completed a MSc at McGill University under the supervision of Joelle Pineau. His research focuses on Graph Representation Learning as well as large-scale computational social science. He has published papers on Graph Representation Learning in top-tier venues across machine learning and network science (NIPS, ICML, KDD, and WWW) and co-organized a tutorial on the topic at WWW 2018 (i.e., TheWebConf). He is the lead developer of GraphSAGE, a state-of-the-art open-source framework for Graph Representation Learning. He was the SAP Stanford Graduate Fellow 2014- 2018, received the Cozzarelli Best Paper Award from the Proceedings of the National Academy of Sciences (PNAS) in 2017, and his work has been featured in numerous media outlets, including Wired, The New York Times, and The BBC.
  • Reihaneh Rabbany is an Assistant Professor at the School of Computer Science, McGill University. Before that, she was a Postdoctoral fellow at the School of Computer Science, Carnegie Mellon University. She completed her Ph.D. in Computing Science Department at the University of Alberta. Her research is at the intersection of network science, data mining and machine learning, with a focus on analyzing real-world interconnected data, and social good applications. She has been on the organizing committee for the Broadening Participation in Data Mining workshop at the ACM SIGKDD 2017 Conference on Knowledge Discovery and Data Mining.
  • Vincent Gripon is a permanent researcher with IMT-Atlantique (Institut Mines-Telecom), Brest, France. He obtained his M.S. from École Normale Supérieure of Cachan and his Ph.D. from Télécom Bretagne. His research interests lie at the intersection of graph signal processing, machine learning and neural networks. He co-authored more than 70 papers in the above-mentioned domains. Since October 2018, he is an invited professor at Université de Montréal.