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Systematic identification of statistically significant network measures | | Etay Ziv
; Robin Koytcheff
; Manuel Middendorf
; Chris Wiggins
; | | Date: |
24 Jun 2003 | | Journal: | Phys. Rev. E 71, 016110 (2005) DOI: 10.1103/PhysRevE.71.016110 | | Subject: | Disordered Systems and Neural Networks; Statistical Mechanics | cond-mat.dis-nn cond-mat.stat-mech q-bio | | Abstract: | We present a novel graph embedding space (i.e., a set of measures on graphs) for performing statistical analyses of networks. Key improvements over existing approaches include discovery of "motif-hubs" (multiple overlapping significant subgraphs), computational efficiency relative to subgraph census, and flexibility (the method is easily generalizable to weighted and signed graphs). The embedding space is based on {it scalars}, functionals of the adjacency matrix representing the network. {it Scalars} are global, involving all nodes; although they can be related to subgraph enumeration, there is not a one-to-one mapping between scalars and subgraphs. Improvements in network randomization and significance testing--we learn the distribution rather than assuming gaussianity--are also presented. The resulting algorithm establishes a systematic approach to the identification of the most significant scalars and suggests machine-learning techniques for network classification. | | Source: | arXiv, cond-mat/0306610 | | Other source: | [GID 575863] pmid15697661 | | Services: | Forum | Review | PDF | Favorites | |
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