There is a great deal of interest in analyzing data that is best represented as a graph.
Examples include the WWW, social networks, biological networks, communication networks,
transportation networks, energy grids, and many others. These graphs are typically
multi-modal, multi-relational and dynamic. In the era of big data, the importance of being
able to effectively mine and learn from such data is growing, as more and more structured
and semi-structured data is becoming available. The workshop serves as a forum for
researchers from a variety of fields working on mining and learning from graphs to share and
discuss their latest findings.
There are many challenges involved in effectively mining and learning from this kind of data, including:
Traditionally, a number of subareas have contributed to this space: communities in graph mining, learning from structured data, statistical relational learning, inductive logic programming, and, moving beyond subdisciplines in computer science, social network analysis, and, more broadly network science.
This workshop is a forum for exchanging ideas and methods for mining and learning with graphs, developing new common understandings of the problems at hand, sharing of data sets where applicable, and leveraging existing knowledge from different disciplines. The goal is to bring together researchers from academia, industry, and government, to create a forum for discussing recent advances in graph analysis. In doing so, we aim to better understand the overarching principles and the limitations of our current methods and to inspire research on new algorithms and techniques for mining and learning with graphs.
To reflect the broad scope of work on mining and learning with graphs, we encourage submissions that span the spectrum from theoretical analysis to algorithms and implementation, to applications, empirical studies and reflection papers. As an example, the growth of user-generated content on blogs, microblogs, discussion forums, product reviews, etc., has given rise to a host of new opportunities for graph mining in the analysis of social media. More recently, the advent of neural methods for learning graph representations has spurred numerous works in embedding network entities for diverse applications including ranking and retrieval, traffic routing and drug-discovery. We encourage submissions on theory, methods, and applications focusing on a broad range of graph-based approaches in various domains.
Topics of interest include, but are not limited to:
We welcome many kinds of papers, such as, but not limited to:
Authors should clearly indicate in their abstracts the kinds of submissions
that the papers belong to, to help reviewers better understand their contributions.
All papers will be peer reviewed, single-blinded. Submissions must be in PDF, no more than 8 pages long — shorter papers are welcome — and formatted according to the standard double-column ACM Proceedings Style.
The accepted papers will be published on the workshop’s website and will not be considered archival for resubmission purposes.
Authors whose papers are accepted to the workshop will have the opportunity to participate in a spotlight and poster session, and some set will also be chosen for oral presentation.
For paper submission, please proceed to the submission website.
Please send enquiries to firstname.lastname@example.org.
To receive updates about the current and future workshops and the Graph Mining community, please join the Mailing List, or follow the Twitter Account.
Paper Submission Deadline: May 30, 2023
Author Notification: June 23, 2023
Camera Ready: July 10, 2023
Workshop: August TBD, 2023
Lead Research Scientist
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