Mining and Learning with Graphs (MLG 2006)
Workshop held with ECML/PKDD 2006 in Berlin
At a time where the amount of data collected day by day far exceeds the human capabilities to extract the knowledge hidden in it, it becomes more and more important to automate the process of learning. Data collections that 'hide' knowledge from us include observations recorded at laboratories as well as businesses' data warehouses. These collections have two things in common: They are huge and the information stored in them is highly structured.
While there has been a continuous need in learning from structured data, recently, there seems to be a surge of interest. Learning from structured data comes in many guises: (Multi-) Relational Data Mining, Graph Mining, Inductive Logic Programming, Learning from Nonvectorial Data, and many more. Most of these are distinguished by the data structure used for knowledge representation. More importantly, it is now common to distinguish two types of structured data:
- The pieces of information are independent from each other but themselves highly structured (internal structure), e.g., molecules; or
- the pieces of informations are parts of a bigger structure (external structure), say pages in the world-wide-web or individuals in a social network.
Graphs are one of the most popular representations in mathematics, computer science, engineering disciplines, and other natural sciences.
The MLG 2006 workshop, which is the successor of the MGTS workshop series on Mining Graphs, Trees and Sequences, will thus concentrate on mining and learning from graphs and its subclasses such as - but not limited to - trees, sequences, strings, ... The primary goal of this workshop is to bring together researchers working on various aspects of learning with graphs, trees, sequences.
Paper submission deadline: June 28th, 2006 July 5th, 2006
Paper acceptance notification: July 26th, 2006 July 28th, 2006
Paper camera-ready deadline: August 9th, 2006
Workshop: September 18th, 2006