The social structure of animal societies can be instrumental to the evolution and maintenance of animal behaviour. Animal social networks (ASNs) provide a framework with which to visualise, quantify and analyse animals' social structure. The work in this thesis incorporates two areas of ASN research. The first area is the analysis of sparse group-derived data. Observation of group memberships is a widely used method to uncover social preferences. Here this method is used to probe the social structure of a population of Trinidadian guppies (Poecilia reticulata). The network is analysed to ascertain if genetic relatedness may play a role in governing social structure. The bright colourings of male fish are also analysed to see if colour influences male-male associations. The guppy study provided motivation for an investigation into association indices for group-derived data. Existing indices are evaluated using a simulated dataset and a new index is proposed. The second part of this thesis contributes to a new and exciting trend in ASNs in which complete records of animal associations are obtained enabling temporal network analysis to be used. This is applied to a population of New Caledonian crows (Corvus moneduloides) which are of interest particularly for their ability to manufacture and use tools for foraging. Emulations of information flow through the network are used to assess the network's information flow potential. A network structure in which information can spread rapidly could indicate that crows can potentially learn tool use skills from their peers.