GRLA2018

International Workshop on Large Scale Graph Representation Learning and Applications

Held in conjunction with IEEE ICDM 2018, November 17-20, 2018 in Singapore

Introduction

Graph is a common representation for relational data. Typical graphs range from professional networks (e.g., LinkedIn), social networks (e.g., Facebook), bibliography networks (e.g., DBLP), to e-commerce networks (e.g., Taobao) and so on. Graph representation learning is a powerful technology to turn the graphs into useful insights. It enables many meaningful applications, such as link prediction, semantic proximity search, user profiling, friend recommendation, and social advertising. We propose to organize a workshop on large-scale graph representation learning and applications (GRLA), in association with The IEEE International Conference on Data Mining (ICDM) 2018. This workshop aims to: 1) contribute to the research development of graph representation learning, and 2) provide a communication platform for both research and industry communities to explore the useful application scenarios in practice.

Important Dates

  • Submission deadline: August 7, 2018
  • Acceptance notification: September 4, 2018
  • Camera-ready deadline and copyright forms: September 15, 2018
  • Conference dates: November 17, 2018

Topics

The theme of this workshop focuses on graph representation learning and its large-scale applications. Topics of interest include, but not limited to:

  • Graph Embedding Methods
    • Graph embedding under various inputs, such as homogeneous vs. heterogeneous graphs, static vs. dynamic graphs, original vs. attributed graphs;
    • Graph embedding under various outputs, such as node embedding, edge embedding, community embedding, subgraph embedding;
    • Deep learning on graphs, such as graph auto-encoder, graph convolution, deep graph kernels;
    • Probabilistic graph inference algorithms, especially when coupled with graph embedding (e.g., embedding propagation);
    • Graph embedding with explainability;
    • Integration of graph embedding models with structured prediction problems and probabilistic graphical models;
  • Graph Construction and Processing Algorithms
    • Graph construction from noisy machine logs, text and social data;
    • Knowledge graph construction, representation and inference;
    • Frequent subgraph pattern mining and instance matching;
    • Graph sampling and summarization;
  • Large-scale Graph-based Applications
    • Network user profiling;
    • Network user relation profiling;
    • Network link prediction;
    • Semantic proximity search;
    • Network community detection and profiling;
    • Network information diffusion and influence maximization;
    • Graph-based recommendation;
  • Implementation of Large-scale Graph Embedding Systems
    • Graph databases;
    • Distributed graph embedding and inference designs;
    • Case studies of real-world applications;

Submission

 

  • Submission
    • Papers including research results, position papers, and practice and experience reports, up to a maximum of four (4) pages, in the IEEE 2-column format, including bibliography and appendices.
    • Demo proposals describing a prototype of system, up to a maximum of two (2) pages, in the IEEE 2-column format, including bibliography and appendices.
    • Online paper submission site is ICDM-GRLA2018.
  • Review process
    • All submissions will be single-blind reviewed by the Program Committee on the basis of technical quality, relevance to scope of the conference, originality, significance, and clarity.
    • All submissions should clearly present the author information including the names of the authors, the affiliations and the emails.
  • Accepted papers
    • Accepted papers will be included into workshop proceedings.
    • At least one author of each accepted paper is required to register for the ICDM 2018 conference and present their work at the workshop.

Committee

Organizers:

  • Wei Lu (Singapore University of Technology and Design, Singapore)
  • Vincent W. Zheng (Advanced Digital Sciences Center, Singapore)
  • Zhao Li (Alibaba Group, China)
Program Committee:

  • Hongxia Yang (Alibaba Group, China)
  • Jianzong Wang (Ping An Technology, China)
  • Shaosheng Cao (Ant Financial, China)
  • Yuan Fang (DBS Bank, Singapore)
  • Yuchen Li (Singapore Management University, Singapore)
  • Zhou Zhao (Zhejiang University, China)
  • Chuan Shi (Beijing Post and Telecommunications University, China)
  • Erik Cambria (Nanyang Technological University, Singapore)
  • Yu Lu (Beijing Normal University, China)
  • Defu Lian (University of Electronic Science and Technology of China, China)
  • Min Wu (A*STAR, Singapore)
  • Xiang Ren (University of South California)
  • Qiongkai Xu (Australian National University, Australia)
  • Chuan-Ju Wang (Academia Sinica, Taiwan)
  • Ming-Feng Tsai (National Chengchi Unviersiyt, Taiwan)