Introduction
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. In the era of big data, 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. The workshop serves as a forum for researchers from a variety of fields working on mining and learning from graphs to share and discuss their latest findings.
Call for paper
Important date
Submission Topics
Topics of interest include, but are not limited to:
Theoretical aspects:
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Computational or statistical learning theory related to graphs
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Theoretical analysis of graph algorithms or models
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Sampling and evaluation issues in graph algorithms
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Relationships between MLG and statistical relational learning or inductive logic programming
Algorithms and methods:
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Graph mining
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Kernel methods for structured data
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Probabilistic and graphical models for structured data
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(Multi-) Relational data mining
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Methods for structured outputs
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Statistical models of graph structure
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Combinatorial graph methods
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Spectral graph methods
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Semi-supervised learning, active learning, transductive inference, and transfer learning in the context of graph
Applications and analysis:
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Analysis of social media
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Social network analysis
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Analysis of biological networks
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Large-scale analysis and modeling
All Comments
Important Date
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Aug 14
2017
Conference Date
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Jun 02 2017
Draft paper submission deadline
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Jun 23 2017
Draft Paper Acceptance Notification
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Jul 07 2017
Final Paper Deadline
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Aug 14 2017
Registration deadline
Sponsored By
Contact Information
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