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CFP: Machine Learning with Graphs (Applied Network Science Journal Special Issue)
Date: October 29, 2018 05:04PM

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



https://appliednetsci.springeropen.com/cfp-mlgraphs

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
Theory
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.

Important Dates

Abstract submission: Dec 20, 2018
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.
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, regardless of the special Issue publication date.

Guest Editors

Austin Benson, Computer Science Department, Cornell University, arb@cs.cornell.edu
Ciro Cattuto, ISI Foundation, ciro.cattuto@isi.it
Shobeir Fakhraei, Information Sciences Institute, Univ. of Southern California, fakhraei@usc.edu
Danai Koutra, Computer Science & Engineering, University of Michigan, dkoutra@umich.edu
Vagelis Papalexakis, Computer Science & Engineering, UC Riverside, epapalex@cs.ucr.edu
Jiliang Tang, Computer Science & Engineering Dept., Michigan State Univ., tangjili@msu.edu

For more information, please direct your questions to the Lead Guest Editor:
Shobeir Fakhraei fakhraei@usc.edu

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