Connectionists: CFP: Machine Learning with Graphs (Applied Network Science Journal Special Issue)

[Shobeir Fakhraei]

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


   -

   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 at cs.cornell.edu
Ciro Cattuto, ISI Foundation, ciro.cattuto at isi.it

Shobeir Fakhraei, Information Sciences Institute, Univ. of Southern
California, fakhraei at usc.edu

Danai Koutra, Computer Science & Engineering, University of Michigan,
dkoutra at umich.edu

Vagelis Papalexakis, Computer Science & Engineering, UC Riverside,
epapalex at cs.ucr.edu

Jiliang Tang, Computer Science & Engineering Dept., Michigan State Univ.,
tangjili at msu.edu

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

Shobeir Fakhraei fakhraei at usc.edu
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