Abstract
The link structure of a networked information space can be used to estimate similarity between nodes. A recursive definition of similarity arises naturally: two nodes are judged to be similar if they have similar neighbours. Quantifying similarity defined in this manner is challenging due to the tendency of the system to converge to a single point (i.e. all pairs of nodes are completely similar).
We present an embedding of undirected graphs into R n based on recursive node similarity which solves this problem by defining an iterative procedure that converges to a non-singular embedding. We use the spectral decomposition of the normalized adjacency matrix to find an explicit expression for this embedding, then show how to compute the embedding efficiently by solving a sparse system of linear equations.
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© 2003 Springer-Verlag Berlin Heidelberg
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Grossman, J.P. (2003). Recursive Node Similarity in Networked Information Spaces. In: Böhme, T., Heyer, G., Unger, H. (eds) Innovative Internet Community Systems. IICS 2003. Lecture Notes in Computer Science, vol 2877. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39884-4_9
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DOI: https://doi.org/10.1007/978-3-540-39884-4_9
Publisher Name: Springer, Berlin, Heidelberg
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