In this paper, we examine the impact of departure from the common assumption of proportionate (random) mixing upon the efficacy of contact tracing. Proportionate mixing assumes that the probability of any two nodes being connected is proportional to the product of their number of contacts. This is unlikely in many cases where 'like attaches to like, assortatively mixed' (e.g. STI transmission; Anderson et al. 1990; Catania et al. 1992) or where 'opposites attract, disassortatively mixed' (e.g. livestock trading among farms and markets in the UK ; correlation properties of the Internet (Pastor-Satorras et al. 2001) ). The empirical evidence and importance of connectivity correlations have led to numerous network-and differential equation-based models (Boguñá et al. 2003a; Newman 2003a; Barthélemy et al. 2005) . Many of these models focus on the effect of connectivity correlations on the epidemic threshold, initial growth rate and hierarchical spread. For example, it has been shown that epidemics on networks characterized by high node degree variance grow rapidly, and in the limiting case of infinite variance, instantaneously, independently of the mixing pattern (Boguñá et al. 2003b) . The initial growth of the epidemic changes from exponential to power law if non-random mixing is combined with small-world properties, with the powerlaw exponent determined by the average node distance on the network ( Vazquez 2006) . While in the limit of infinite populations, the implications of preferential mixing for disease invasion are well known, here we focus on disease control and consider the efficacy of contact tracing for different mixing patterns. For networks that are poorly characterized, the efficacy of contact tracing is difficult to determine without exact knowledge of the underlying contact network, i.e. who is connected to whom? By analysing disease transmission on theoretical networks with different mixing patterns, we aim to identify the implications of non-random mixing for epidemic dynamics and control strategies.