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.