Graph mining algorithms that seek to find interesting structure in a graph are compelling for many reasons but may not lead to useful information learned from the data. This position paper explores the current graph mining approaches and suggests why certain algorithms may provide misleading information whereas others may be just what is needed. In particular, algorithms that ignore the rich set of node and edge properties that are prevalent in many real-world graphs are in danger of finding results based on the wrong information.
Revised: December 13, 2013 |
Published: May 20, 2013
Citation
Haglin D.J., and L. Holder. 2013.Combining Structure and Property Values is Essential for Graph-based Learning. In IEEE 27th International Parallel and Distributed Processing Symposium Workshops & PhD Formum (IPDPSW 2013), May 20-24, 2013, Cambridge, MA, 1899-1904. Piscataway, New Jersey:Institute of Electrical and Electronics Engineers.PNNL-SA-94099.doi:10.1109/IPDPSW.2013.44