NDLI: A Comparison of Graph Embedding Methods for Vertex Nomination

Content Provider	IEEE Xplore Digital Library
Author	Ming Sun Minh Tang Priebe, C.E.
Copyright Year	2012
Abstract	Given an attributed graph representation of data, vertex nomination works to find the group of vertices which are of interest, e.g., those vertices whose attributes are different from others', or the connection among those vertices are more frequent. In this paper we present an algorithm to estimate the power of nominating these interesting vertices. This algorithm is based on Wilcoxon rank sum test. It requires to embed graph vertices into a low dimensional space. Two graph embedding methods, adjacency spectral embedding and multidimensional scaling composed with canonical correlation analysis are employed. We investigate a case where two graphs are available for modeling the same objects in different spaces, and show the effects of data fusion on vertex nomination power.
Starting Page	398
Ending Page	403
File Size	1349528
Page Count	6
File Format	PDF
ISBN	9781467346511
DOI	10.1109/ICMLA.2012.72
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wilcoxon test data fusion Electronic publishing Correlation Image edge detection vertex nomination Encyclopedias Vectors adjacency spectral embedding canonical correlation analysis Internet power multidimensional scaling
Content Type	Text
Resource Type	Article

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