  The citation network constituted by the SPIRES data base is investigated
empirically. The probability that a given paper in the SPIRES data base has $k$
citations is well described by simple power laws, $P(k) \propto k^{-\alpha}$,
with $\alpha \approx 1.2$ for $k$ less than 50 citations and $\alpha \approx
2.3$ for 50 or more citations. Two models are presented that both represent the
data well, one which generates power laws and one which generates a stretched
exponential. It is not possible to discriminate between these models on the
present empirical basis. A consideration of citation distribution by subfield
shows that the citation patterns of high energy physics form a remarkably
homogeneous network. Further, we utilize the knowledge of the citation
distributions to demonstrate the extreme improbability that the citation
records of selected individuals and institutions have been obtained by a random
draw on the resulting distribution.
