International Workshop on Large Scale Graph Representation Learning and Applications
Held in conjunction with IEEE ICDM 2018, November 17-20, 2018 in Singapore
- 8:30am – 9:15am Invited Talk
- Title: TBD
- Speaker: Yangqiu Song (Hong Kong University of Science and Technology)
- 9:15am – 10:00am Invited Talk
- Title: TBD
- Speaker: Xia Hu (Texas A&M University)
- 10:00am – 10:30am Coffee Break & Poster Session
- 10:30am – 11:15am Invited Talk
- Title: TBD
- Speaker: TBD
- 11:15am – 12:00pm Round Table Discussion
- Invited speakers, organizers and attendees
- Submission deadline:
August 7, 2018Aug 11, 2018, 23:59:59 (USA Eastern Standard Time)
- Due to receiving multiple separate requests, the organizing committee has carefully reviewed the situation and decided to extend the deadline.
- Acceptance notification: September 4, 2018
- Camera-ready deadline and copyright forms: September 15, 2018
- Conference dates: November 17, 2018
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;
- Long paper: maximum eight (8) pages, in the IEEE 2-column format, including bibliography and appendices.
- Short paper: maximum four (4) pages, in the IEEE 2-column format, including bibliography and appendices.
- All accepted papers will be included in the IEEE ICDM 2018 Workshops Proceedings volume published by IEEE Computer Society Press, and will also be included in the IEEE Xplore Digital Library. Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.
- Online paper submission site is ICDM-GRLA2018. There is no separate site for short-paper and long-paper.
- 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.
- Vincent W. Zheng (Advanced Digital Sciences Center, Singapore)
- Wei Lu (Singapore University of Technology and Design, Singapore)
- Zhao Li (Alibaba Group, China)
- 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 Unviersity, Taiwan)