Machine Learning Journal Special issue on Mining and Learning with Graphs and Relations

Call for Papers:

Driven by application areas ranging from biology to the World Wide Web, research in Data Mining and Machine Learning is nowadays increasingly focusing on the analysis of structured data. Of particular interest is data that consists of interrelated parts or is characterized by collections of objects that are interrelated and linked together into complex graphs and structures. Dealing with such inter-related data is one of the major research challenges that we are facing. The aim of this special issue is to bring together papers from different sub-disciplines within Machine Learning and Data Mining that focus on the analysis of structured data. We invite high quality submissions from researchers in all areas of machine learning and data mining working on "mining and learning with graphs and relations", in particular but not limited to, the following areas: Application areas of interest are also diverse and include (but not limited to):

Submission procedure:

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals. Manuscripts should be submitted to: https://cmt.research.microsoft.com/LMGSI This online system offers easy and straightforward log-in and submission procedures, and accepts pdf and doc file formats. It also offers authors the ability to track the review process of their manuscript. No special formatting is necessary - should your paper be accepted for publication, Springer's production team will convert your editable source files to match the journal style.

Important Dates:

Guest Editors: