CALL FOR PAPERS
Machine Learning Special Issue on
MINING AND LEARNING WITH GRAPHS

Graphs are one of the most popular representations in mathematics, computer science, engineering disciplines, and other natural sciences. Recently, there has been an increasing interest in the topic of mining and learning from graphs and their subclasses such as trees and sequences. This is witnessed, e.g., by the increasing number of submissions to the ECML/PKDD workshop on Mining and Learning with Graphs (MLG, formerly MGTS) and the KDD workshop on Link Analysis (LinkKDD). Following the successful history of these workshops since 2003, the journal Machine Learning invites authors to submit papers for a special issue on Mining and  Learning with Graphs. Authors are invited to submit original results on:

Contributions to prominent application areas dealing with graphs, such as social network analysis, biological networks analysis, or web mining, are encouraged as well as contributions dealing with important subclasses of graphs. Submission to the special issue is open to everyone. Each submission will be reviewed according to the standards of Machine Learning.

Manuscripts submitted to the Machine Learning Special Issue on Mining and Learning with Graphs, should be formatted according to the Springer style files. Authors may consult the Machine Learning Information for authors. Only electronic submissions will be accepted. Please do not send submissions to the guest editors but use Springer's online submission system.

Submissions must not have appeared in, nor be under consideration by, other journals. Authors of papers that have appeared previously in refereed conferences and workshops may submit extended versions of their papers to the special issue but must clearly indicate this. Such extended papers must be significantly different from the conference version, as well as accessible to the broad readership of the journal.

All authors of accepted papers are required to sign a Publication Agreement that grants Springer a six months exclusive commercial distribution license. The copyright itself remains with the authors who will be free to make their paper available from their web page.