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
Heterogeneous graph embedding
Knowledge graph embedding
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
|14:05-14:45||Keynote Speaker: Xing Xie (Microsoft Research Asia) Title: Knowledge Enhanced Recommendation Systems|
|16:20-17:00||Keynote Speaker: Shirui Pan (Monash University) Title: Towards Dynamic and Efficient Graph Neural Networks|
Node-Feature Convolution for Graph Convolutional Networks.
Li Zhang, Heda Song, Nikolaos Aletras and Haiping Lu
UBER-GNN: A User-Based Embeddings Recommendation based on Graph Neural Networks.
Bo Huang, Ye Bi, Zhenyu Wu, Jianming Wang and Jing Xiao
NISER: Normalized Item and Session Representations with Graph Neural Networks.
Priyanka Gupta, Diksha Garg, Pankaj Malhotra, Lovekesh Vig and Gautam Shroff
A Unified Network Embedding Algorithm for Multi-type Similarity Measures.
Rui Feng, Yang Yang, Yuehan Lyu, Yizhou Sun and Chunping Wang
Student Performance Prediction based on Multi-View Network Embedding.
Jianian Li, Yanwei Yu, Yunhong Lu and Peng Song
(1) Camera-ready submission is not needed because workshop papers will not be included in the ACM Digital Library. This polocy intends to help preserve the authors' ability to submit a revised version of their paper to a conference or journal.
(2) At least one author of each accepted paper must register at a non-student full conference registration rate (as an ACM or non-ACM member).
Submission Deadline: July 31, 2019
Acceptance Notification: September 6, 2019
Workshop Date: November 7, 2019
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
Cheng Yang , Tsinghua University
Jinhua Gao , Chinese Academy of Sciences
Guojie Song, , Peking University
Zhongying Zhao , Shandong University of Science & Technology
Chuan Zhou , Chinese Academy of Sciences
Huayu Wan , Beijing Jiaotong University
Yongqing Wang , Chinese Academy of Sciences
Xiaofei Zhu , Chongqing University of Technology
Xiao Wang , Beijing University of Posts & Telecommunications
Pengfei Wang , Beijing University of Posts & Telecommunications