Call for papers
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:
- Learning and mining algorithms
- Graph mining approaches
- Link and relationship strength prediction
- Learning to rank in networks
- Similarity measures and graph kernel methods
- Graph alignment, matching, and identification
- Network summarization and compression
- Learning from partially-observed networks
- Semi-supervised learning, active learning, transductive inference, and transfer learning in the context of graphs
- Large-scale analysis and models for graph data
- Evaluation issues in graph-based algorithms
- Anomaly detection with graph data
- Embeddings and factorization methods
- Network embedding methods and manifold learning
- Matrix and tensor factorization methods
- Deep learning on graphs
- Learning with dynamic and complex networks
- Models to learn from dynamic graph data
- Heterogeneous, signed, attributed, and multi-relational graph mining methods
- Online learning with graphs
- Statistical and probabilistic methods
- Computational or statistical learning theory related to graphs
- Statistical models of graph structures
- Probabilistic and graphical models for structured data
- Statistical relational learning
- Sampling graph data
- Theoretical analysis of graph-based machine learning algorithms or models
- Combinatorial graph methods
We also encourage submissions focused on machine learning applications that use graph data. Such applications include, but are not limited to
- Biomedicine and medical networks
- Social network analysis
- The World Wide Web
- Neuroscience and neural networks
- Transportation systems and physical infrastructure
- Knowledge graphs
- Recommender systems
Survey and review papers as well as submissions that are significant extension (more than 30%) of previously published work are welcome.
- Abstract submission: Dec 20, 2018
- Abstract feedback notification: Jan 10, 2019
- Paper submission deadline: Mar 1, 2019
- Target publication: Jul 30, 2019
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.
We invite authors to submit a brief expression of interest containing a short outline or extended abstract (approx. 1000 words), including the topic, key concepts, methods, expected results, and conclusions.
Abstracts should be submitted via email to email@example.com, and will be reviewed to determine if the submission is in the scope of this special issue.
Authors with accepted abstracts will be invited to submit their papers through the journal submission system for a full review and publication.
Austin Benson, Computer Science Department, Cornell University, firstname.lastname@example.org
Ciro Cattuto, ISI Foundation, email@example.com
Shobeir Fakhraei, Information Science Institute, Univ. of Southern California, firstname.lastname@example.org
Danai Koutra, Computer Science & Engineering, University of Michigan, email@example.com
Vagelis Papalexakis, Computer Science & Engineering, UC Riverside, firstname.lastname@example.org
Jiliang Tang, Computer Science & Engineering Dept., Michigan State Univ., email@example.com
For more information, please direct your questions to the Lead Guest Editor:
Shobeir Fakhraei firstname.lastname@example.org
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22 October 2018
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- ISSN: 2364-8228