LSGDA 2019

The 1st International Workshop on Large Scale Graph Data Analytics

Various application domains such as social networks, communication networks, collaboration networks, biological networks, transportation networks, knowledge networks naturally generate large scale graph data to capture the connectedness among entities. Driven by these applications, there is an increasing demand for the development of novel graph analytics models and scalable graph analytics techniques and systems. The 1st International Workshop on Large Scale Graph Data Analytics (LSGDA 2019) aims to provide a forum for researchers from academia and industry to exchange ideas, techniques and application scenarios in large scale graph data analytics as well as discuss open challenges and identify new research directions in the area. Besides regular research papers, we also welcome vision papers, demonstration papers and papers with industry showcase from various applications.

The workshop will be of interest to researchers in developing techniques for large scale graph data analytics in various application domains. The intended audiences include researchers from both academia and industry who are interested in exploiting the value of large scale graph data.

Research Interest

Topics of interest include but not limited to:

  • Graph data model, storage, indexing and query processing techniques
  • Graph mining techniques
  • Techniques for distributed graph analytics
  • Graph visualization techniques and system interfaces
  • Dynamic and streaming graph data analytics
  • Spatial-temporal graph analytics
  • AI techniques for graphs
  • Machine learning techniques for graphs
  • Graph analytics in various application domains such as social networks multimedia, semantic web, biological data, business processes, transport data, etc.
  • Vision papers to survey the area of graph data analytics as well as describe the future research directions

Submission Guidelines

The proceedings of the workshops will be published jointly with the conference proceedings and all papers must be formatted following the IEEE ICDE 2019 submission guidelines available at the conference webpage. We welcome research papers (full or short), vision papers, demo papers and industry papers showcasing graph analytics in real applications.

Including the bibliography and any possible appendences,

  • Full papers and vision papers should be a maximum of 8 pages in length
  • Short papers, demo papers and industry papers should be a maximum of 4 pages in length.

Please upload your submission to the LSGDA 2019 Research Track through the CMT system at:

Important Dates

Paper submission: December 31, 2018

Paper notification: January 31, 2019

Camera ready deadline: February 22, 2019

Members of the workshop organizers:

General Chair: Jeffrey Xu Yu, Chinese University of Hong Kong HK

PC co-chairs:

  • Lu Qin, University of Technology Sydney Australia
  • Wenjie Zhang, University of New South Wales Australia
  • Ying Zhang, University of Technology Sydney Australia

Members of the program committee:

  • Vijil Chenthamarakshan, IBM AI Research
  • Bolin Ding, Data Analytics and Intelligence Lab, Alibaba Group
  • Yixiang Fang, University of New South Wales
  • Rong-Hua Li, Beijing Institute of Technology
  • Xiaoling Wang, East China Normal University
  • Dong Wen, University of Technology Sydney
  • Chuan Xiao, Nagoya University
  • Weiren Yu, Aston University
  • Zhiwei Zhang, Hong Kong Baptist University
  • Xiang Zhao, National University of Defence Technology
  • Yuanyuan Zhu, Wuhan University
  • Zhaonian Zou, Harbin Institute of Technology
  • Huasong Shan, JD.COM
  • Jiafeng Hu, Google

Workshop Programs




Keynote I

Lei Zou (Peking University)

Keynote II

Yinglong Xia (Huawei Research America)

Coffee Break


Session I: Graph & Algorithms

Reachability in Large Graphs using Bloom Filters

Arkaprava Saha (Nanyang Technological University), Neha Sengupta (IIT Delhi), Maya Ramanath (IIT Delhi)

Triangle counting on GPU using fine-grained task distribution

Lin Hu (Peking University), Naiqing Guan (Peking University), Lei Zou (Peking University)

Efficient Parallel Computing of Graph Edit Distance

Ran Wang (East China Normal University), Yixiang Fang (University of New South Wales), Xing Feng (Bloomberg)

MPMatch: A Multi-Core Parallel Subgraph Matching Algorithm

Xin Jin (East China Normal University), Longbin Lai (University of New South Wales)


Lunch Break


Session II: Graph & Applications

Improving Distribued Subgraph Matching Algorithm on Timely Dataflow

Zhengmin Lai (East China Normal University), Zhengyi Yang (University of New South Wales, Sydney), Longbin Lai (University of New South Wales, Sydney)

Classification of Medical Images with Synergic Graph Convolutional Networks

Bin Yang (Harbin Engineering University), Haiwei Pan (Harbin Engineering University)

Skyline Nearest Neighbor Search on Multi-Layer Graphs

Wanqi Liu (University of Technology Sydney), Dong Wen (University of Technology Sydney), Hanchen Wang (University of Technology Sydney), Fan Zhang (University of New South Wales), Xubo Wang (University of Technology Sydney)

Semantic Similarity Computation in Knowledge Graphs: Comparisons and Improvements

Chaoqun Yang (Wuhan University), Yuanyuan Zhu (Wuhan University), Ming Zhong (Wuhan University), Rongrong Li (Wuhan University)


Coffee Break


Session III: Social Networks

A Method for Scalable First-Order Rule Learning on Twitter Data

Monica Senapati (University of Missouri-Kansas City), Laurent Njilla (Air Force Research Lab), Praveen Rao (University of Missouri-Kansas City)

Elites Tweet? Characterizing the Twitter Verified User Network

Indraneil Paul (IIIT Hyderabad), Abhinav Khattar (IIIT Delhi), Ponnurangam Kumaraguru (IIIT Hyderabad), Manish Gupta (Microsoft Bing), Shaan Chopra (Indraprastha Institute of Information Technology, Delhi)

Generating Synthetic Graphs for Large Sensitive and Correlated Social Networks

Ju Xin (Ju Xin), Xiaofeng Zhang (Harbin Institute of Technology) , William Cheung (Hong Kong Baptist University)

Processing SPARQL Queries Over Distributed RDF Graphs -
A Partial Evaluation and Assembly Approach

Prof. Lei Zou

Peking University


We propose techniques for processing SPARQL queries over a large RDF graph in a distributed environment. We adopt a "partial evaluation and assembly" framework. Answering a SPARQL query Q is equivalent to finding subgraph matches of the query graph Q over RDF graph G. Based on properties of subgraph matching over a distributed graph, we introduce local partial match as partial answers in each fragment of RDF graph G. For assembly, we propose two methods: centralized and distributed assembly.

Short Biography

Lei Zou received his BS degree and Ph.D. degree in Computer Science at Huazhong University of Science and Technology (HUST) in 2003 and 2009, respectively. He received a CCF (China Computer Federation) Doctoral Dissertation Nomination Award in 2009, won Second Class Prize of CCF Natural Science Award in 2014 and Second Class Prize of Natural Science of the Ministry of Education, China in 2017. Since September 2009, he joined Institute of Computer Science and Technology (ICST) of Peking University (PKU) as a faculty member. He has been a professor in PKU since August 2017. Before joining PKU, he visited Hong Kong University of Science and Technology in 2007 and University of Waterloo in 2008 as a visiting scholar. His recent research interests include graph databases, knowledge graph, particularly in graph-based RDF data management. He has published more than 50 papers, including more than 30 papers published in reputed journals and major international conferences, such as SIGMOD, VLDB, ICDE, TODS, TKDE, VLDB Journal. Lei Zou's research is supported by NSFC-Young Excellent Talent Project and National Key Research and Development Program of China.

Architecting a Comprehensive Enterprise Graph Platform

Dr. Yinglong Xia

Huawei Research America


Graph technology has been playing increasingly important roles in various machine learning, data analytics, and resource management domains, thus more and more companies have been adopting/utilizing graph platforms, either on cloud or on premise, to support their business. In this talk, we will investigate various factors that contribute to the success of a graph platform for enterprise use, ranging from graph data organization, runtime scheduling, analytics optimization, to some thoughts on recent graph deep learning frameworks. We will discuss through some concrete examples on how to effectively put together the above building blocks, so as to form a comprehensive graph platform delivering efficient end-to-end performance to meet the requirements in many industrial scenarios. At last, we will brief some recent activities in graph technology community, such as the discussions on its standardization, along with the summary of the challenges and opportunities.

Short Biography

Yinglong Xia is a chief architect at Huawei Research America, working on AI platforms and Graph Engine Service (GES, Prior to that, he was a technical leader and research staff member at IBM Watson Research Center, exploring graph database and reasoning framework, creating the IBM System G ( platform. He has solid experience in both industrial research and product development, already published 60+ technical papers and filed 30+ patents. He serves as a technical advisory committee (TAC) member in Linux Foundation, a board member of LDBC, and an associate editor of IEEE trans. Knowledge and Data Engineering (TKDE), and IEEE trans. Big Data (TBD); he is a general co-chair of IEEE HiPC'19, vice co-chair of IEEE BigData'19, TPC member of KDD'19, VLDB'19, and ICDE'19, etc.