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Call for Papers:
Applied Network Science Special Issue on
Machine Learning with Graphs


Data that are best represented as a graph such as social, biological, communication, or transportation networks, and energy grids are ubiquitous in our world today. As more of such structured and semi-structured data is becoming available, the machine learning methods that can leverage the signal in these data are becoming more valuable, and the importance of being able to effectively mine and learn from such data is growing.

These graphs are typically multi-relational, dynamic, and large-scale. Understanding the different techniques applicable to graph data, dealing with their heterogeneity and applications of methods for information integration and alignment, handling dynamic and changing graphs, and addressing each of these issues at scale are some of the challenges in developing machine learning methods for graph data that appear in a variety of applications.

In this special issue, we aim to publish articles that help us better understand the principles, limitations, and applications of current graph-based machine learning methods, and to inspire research on new algorithms, techniques, and domain analysis for machine learning with graphs.  

We encourage submissions on theory, methods, and applications focusing on a broad range of graph-based machine learning approaches in various domains. Topics of interest include but are not limited to theoretical aspects, algorithms, and methods such as:

We also encourage submissions focused on machine learning applications that use graph data. Such applications include, but are not limited to:

Survey and review papers as well as submissions that are significant extension (more than 30%) of previously published work are welcome.

Important Dates

We encourage to submit the papers prior to these deadlines. Papers will be subject to a fast track review procedure that will start as soon as they are submitted, and are published upon acceptance, regardless of the special Issue publication date.

Guest Editors

Austin Benson, Computer Science Department, Cornell University, [log in to unmask]
Ciro Cattuto, ISI Foundation, [log in to unmask]

Shobeir Fakhraei, Information Sciences Institute, Univ. of Southern California, [log in to unmask]  

Danai Koutra, Computer Science & Engineering, University of Michigan, [log in to unmask]

Vagelis Papalexakis, Computer Science & Engineering, UC Riverside, [log in to unmask]

Jiliang Tang, Computer Science & Engineering Dept., Michigan State Univ.,  [log in to unmask]

For more information, please direct your questions to the Lead Guest Editor:

Shobeir Fakhraei [log in to unmask]

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