Introduction

Graph Representation Learning & its Applications

G

raph structured data are ubiquitous nowadays in a variety of disciplines and domains ranging from computer science, social science, economics, medicine, to bioinformatics. Examples include social networks, knowledge graph, e-commerce networks, protein-protein interaction graphs, and molecular structures. Recently, representation learning for graphs has attracted considerable attention from researchers and communities, and led to state-of-the-art results in numerous tasks including molecule classification, new drug discovery, recommender systems, etc. This workshop aims to provide a forum for industry and academia to discuss the latest progress on graph representation learning and their applications in different fields. We expect novel research works that address various aspects of graph representation learning, including learning representations of entire graph, knowledge graph embedding, graph neural networks, applications in information network analysis, natural language understanding, recommender systems, drug discovery, and so on.

The theme of this workshop is to explore graph representation learning methods or technologies for information and knowledge management. In particular, topics of interest include but are not limited to:

Unsupervised node representation learning
Learning representations of entire graphs
Graph neural networks
Graph generation
Heterogeneous graph embedding
Knowledge graph embedding
Graph alignment
Graph matching
Dynamic graph representation

Graph representation learning for relational reasoning
Graph anomaly detection
Applications in recommender systems
Applications in information network analysis
Applications in natural language understanding
Applications in traffic predictions
Applications in social network analysis
Applications in drug discovery
Other applications

Schedule

 

Morning Sessions

Session 1: Workshop introduction and keynote speech
Session 2: Paper presentation

Afternoon Sessions

Session 3: Paper presentation
Session 4: Discussion panel

 

Speakers

To Be Annouced Soon!

To Be Annouced Soon!

Important Dates

 

Submission Deadline: July 22, 2019 (tentative date)

Acceptance Notification: August 21, 2019 (tentative date)

Camera-Ready Submission: August 31, 2019

Workshop Date: November 3, 2019

 

Workshop Organizers

Huawei Shen

Institute of Computing Technology, Chinese Academy of Sciences, China, shenhuawei@ict.ac.cn

Jian Tang

HEC Montreal and Montreal Institute for Learning Algorithms, Canada, tangjianpku@gmail.com

Peng Bao

Beijing Jiaotong University, China, baopeng@bjtu.edu.cn

Program Committee

 

• Peng Cui, Tsinghua University
• Yuxiao Dong, Microsoft Research, Redmond
• Meng Jiang, University of Notre Dame
• Zhiyuan Liu, Tsinghua University
• Shenghua Liu, Chiense Academy of Sciences
• Chuan Shi, Beijing University of Posts and Telecommunications
• Jie Tang, Tsinghua University
• Junchi Yan, Shanghai Jiao Tong University
• Chao Zhang, Georgia Tech
• Xin Zhao, Renmin University of China