Graphs are widely used to model complex data nowadays: social networks, recommendation engines, computer networks, bio-informatics, to name a few. Graph theory has traditionally been a core area of research in computer science and artificial intelligence. With the growing number of applications and amount of data, Graph Computing addresses the storage, analysis, synthesis, and processing of (possibly large) graphs to solve complex problems. . 

The first International Conference on Graph Computing  (GC 2019) is an international forum for academia and industries to exchange visions and ideas in the state of the art and practice of Graph Computing, as well as to identify the emerging topics and define the future of Graph Computing.

Call for paper

Call for paper description

We welcome workshop proposals on topics within the scope of GC 2019, emerging research areas and applications, multi-discipline topics, and other miscellaneous topics that are well suited to the flexibility that a workshop format provides and that are expected to be of interest to a significant proportion of GC 2019 attendees. Workshops may consist of any subset of invited talks, paper presentations, discussion panels, and other activities that the workshop organizers would like to propose. The rooms provided for the workshops will have capacities ranging from 30 to 60. Papers submitted to the workshops for peer review should consist of new unpublished work and papers accepted to the workshops will be published in the workshops section of the GC proceedings.

Topics of submission

Graph theory

Storage of graphs

Combinatorial optimization

Knowledge graph

Network analysis

Diffusion analysis

Influence analysis

Social network

Semantic network

Graph database

Query language

Query processing and optimization

Integrity and security

Graph OLAP

Graph mining

Graph learning

Graph reduction

Big graphs



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Important dates

  • Conference Dates

    25 Sep.



    27 Sep.


Contact information


Sponsored By

  • University of California, Irvine

Supported By

  • IEEE Computer Society
    Institute for Semantic Computing