Strategic Network Disruption and Defense

Networks are one of the essential building blocks of society. Not only do firms cooperate in R&D networks, but firms themselves may be seen as networks of information-exchanging workers. Social movements increasingly make use of networks to exchange information, just as on the negative side criminal and terrorist networks use them. However, the literature on networks has mainly focused on the cooperative side of networks and has so far neglected the competition side of networks. Networks themselves may face competition from actors with opposing interests to theirs. Several R&D networks may compete with one another. The firm as a network of employees obviously faces competition. In particular, given the importance of connectivity for networks, competing networks may try to disrupt each other, by trying to convince key players in competing networks to defect, or to stop sponsoring key links (strategic network disruption). In response, networks that face competition will adapt their structure, and will avoid vulnerable network structures. Such network competition is what our paper is concerned with.Strategic Network Disruption, Strategic Network Design, Noncooperative Network Games

Resolving structural variability in network models and the brain

Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar diagnostics presented in statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling---in addition to several summary statistics, including the mean clustering coefficient, shortest path length, and network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be embedded in anatomical brain regions tend to produce distributions that are similar to those extracted from the brain. We also find that network models hardcoded to display one network property do not in general also display a second, suggesting that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data.Comment: 24 pages, 11 figures, 1 table, supplementary material