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The availability of large scale streaming network data has reinforced the ubiquity of power-law distributions in observations and enabled precision measurements of the distribution parameters. The increased accuracy of these measurements allows new underlying generative network models to be explored. The preferential attachment model is a natural starting point for these models. This work adds additional model components to account for observed phenomena in the distributions. In this model, preferential attachment is supplemented to provide a more accurate theoretical model of network traffic. Specifically, a probabilistic complex network model is proposed using preferential attachment as well as additional parameters to describe the newly observed prevalence of leaves and unattached nodes. Example distributions from this model are generated by considering random sampling of the networks created by the model in such a way that replicates the current data collection methods.

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The Explorer-Director game, first introduced by Nedev and Muthukrishnan, can be described as a game where two players-Explorer and Director-determine the movement of a token on the vertices of a graph. At each time step, the Explorer specifies a distance that the token must move hoping to maximize the amount of vertices ultimately visited, and the Director adversarially chooses where to move token in an effort to minimize this number. Given a graph and a starting vertex, the number of vertices that are visited under optimal play is denoted by f d (G, v).In this paper, we first reduce the study of f d (G, v) to the determination of the minimal sets of vertices that are closed in a certain combinatorial sense, thus providing a structural understanding of each player's optimal strategies. As an application, we address the problem on lattices and trees. In the case of trees, we also provide a complete solution even in the more restrictive setting where the strategy used by the Explorer is not allowed to depend on their opponent's responses. In addition to this paper, a supplementary companion note will be posted to arXiv providing additional results about the game in a variety of specific graph families.