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When analyzing weighted networks using spectral embedding, a judicious transformation of the edge weights may produce better results.
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Rohe, K., Chatterjee, S., and Yu, B. (2011) · 1915
Earlier work this paper cites.
Statistical methods for research workers
Fisher, R. A. (1934) · 1934
Earlier work this paper cites.
A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations
Chernoff, H. (1952) · 1952
Earlier work this paper cites.
Sur la division des corp matériels en parties
Steinhaus, H. (1956) · 1956
Earlier work this paper cites.
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Holland, P. W., Laskey, K. B., and Leinhardt, S. (1983) · 1983
Earlier work this paper cites.
Matrix versions of the Cauchy and Kantorovich inequalities
Marshall, A. W. and Olkin, I. (1990) · 1990
Earlier work this paper cites.
Community structure in social and biological networks
Girvan, M. and Newman, M. E. (2002) · 2002
Earlier work this paper cites.
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Stuart, J. M., Segal, E., Koller, D., and Kim, S. K. (2003) · 2003
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Bell, R., Koren, Y., and Volinsky, C. (2007) · 2007
Earlier work this paper cites.
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Mu, C., Mele, A., Hao, L., Cape, J., Athreya, A., and Priebe, C. E. (2020) · 2007
Earlier work this paper cites.
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Earlier work this paper cites.
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Heard, N. A., Weston, D. J., Platanioti, K., and Hand, D. J. (2010) · 2010
Earlier work this paper cites.
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Later among the works it cites.
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