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Combining Structure and Property Values is Essential for Graph-based Learning [Position Paper]

By David J. Haglin and Lawrence B. Holder

Abstract

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. 1

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.4395
Provided by: CiteSeerX
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