20th International Workshop on
Mining and Learning with Graphs
22nd of September 2023
In conjunction with ECMLPKDD 2023
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. 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. Effectively learning from this kind of data poses several challenging problems, 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, social network analysis, and network science. Our workshop will serve as a forum for researchers from this variety of fields working on mining and learning with graphs to share and discuss their latest findings.
Paper Submission Deadline:
June 12th June 18th (11:59pm, AoE) 2023
Author Notification: July 12th 2023
Camera Ready: September 4th 2023
Workshop: September 22rd 2023
Deep learning on graphs tutorial
The tutorial will introduce the audience to the area of deep learning for graphs and its applications. Dealing with graph data requires learning models capable of adapting to structured samples of varying size and topology, capturing the relevant structural patterns to perform predictive and explorative tasks while maintaining the efficiency and scalability necessary to process large scale networks. The tutorial will first introduce foundational aspects and seminal models for learning with graph structured data. Then it will delve into the details of the predominant paradigm in deep learning for graphs, that is neural message-passing, and discuss its reference literature models. It will conclude with a brief discussion on the limitations of these approaches and the research opportunities that are stemming from this.
Torino, Italy (all times are CEST)
All accepted papers will be presented in the poster session.
|09:00||Tutorial by Davide Bacciu|
|11:00||1st & 2nd oral talk|
|12:00||Poster session (group A)|
|14:00||Keynote by Giannis Nikolentzos|
|14:45||3rd & 4th oral talk|
|15:15||Coffee break with topical discussions|
|15:45||Keynote by Bastian Rieck|
|17:00||Poster session (group B)|
|18:00||Conclusion & awards|
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 and industry, 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 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 and empirical studies. We are interested in the full spectrum of graph data, including but not limited to attributed graphs, labeled graphs, knowledge graphs, evolving graphs, transactional graph databases, etc.
We therefore invite submissions on theoretical aspects, algorithms and methods, and applications of the following (non-exhaustive) list of areas:
We welcome many kinds of papers, such as, but not limited to:
Authors must 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 (single blind) peer reviewed.
Submissions must be in PDF, long papers no more than 12 pages long, short papers no more than 8 pages long, formatted according to the standard Springer LNCS style required for ECMLPKDD submissions. Authors can use unlimited additional pages for references and appendices.
All accepted papers will be published on the workshop’s website. Unpublished submissions (e.g., novel research papers, novel demo papers, novel visionary papers, and novel appraisal papers) will additionally be considered for inclusion in the ECMLPKDD workshop proceedings in the Springer CCIT series. This is an opt-in process. Website publication will not be considered archival for resubmission purposes. Authors whose papers are accepted to the workshop will pitch their work to the full audience and will participate in a poster session. The best two submissions will also be chosen for a long oral presentation.
Please note that at least one author of each accepted paper has to register for the conference.
Dual Submission Policy: We accept submissions that are currently under review at other venues. However, in this case our page limits apply. Please also check the dual submission policy of the other venue.
For paper submission, please proceed to our submission page. When submitting a paper make sure to select the correct track “MLG: Mining and Learning with Graphs” from the list of workshops.
Please send enquiries to firstname.lastname@example.org.
This year, the International Workshop on Mining and Learning with Graphs is collocated with two conferences. The ACM SIGKDD Conference on Knowledge Discovery and Data Mining, and the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.
This allows researchers and practitioners from Europe and America to choose a venue that is geographically close and in a suitable time zone.
Feel free to visit the homepage of this year’s sister workshop: