We use multiple measures of graph complexity to evaluate the realism of synthetically-generated networks of human activity, in comparison with several stylized network models as well as a collection of empirical networks from the literature. The synthetic networks are generated by integrating data about human populations from several sources, including the Census, transportation surveys, and geographical data. The resulting networks represent an approximation of daily or weekly human interaction. Our results indicate that the synthetically generated graphs according to our methodology are closer to the real world graphs, as measured across multiple structural measures, than a range of stylized graphs generated using common network models from the literature.
@cite_45 showed that there is a significant difference in how hard it is to shatter social networks as compared to infrastructural networks (transportation, power, and wireless radio networks), by examining the size of the largest component in the network as nodes are deleted in order of (residual) degree. Social networks were shown to be much more robust than the infrastructural networks.
@cite_45
1631038321 2138482845 2155202145 2124444733
Recent studies of attacks on complex networks suggest that small initial breakdowns can lead to global cascades of overload failures in communication, economic trading, and supply-transportation systems, considering the defense methods is very important to reduce the huge damage. The propagation of failures depends on the flow dynamics such as packet routing of physical quantities and on the heterogeneous distribution of load or capacity in many realistic networks which have topological properties of scale-free (SF) and degree-degree correlations. We introduce a defense strategy based on emergent rewirings between neighbors of the attacked node, and investigate the size of cascade on SF networks with controllable correlations. We show the differences of damaged size for the types of correlations and the effective range of tolerance in the defense methods. They can be performed on the current wireless communication or dynamic allocation technologies. Large-scale data networks form the infrastructure for contemporary global communications. Increasingly, a single network may provide a variety of disparate services, flatter architectures (i.e., fewer controlling hubs) are used to achieve robustness against failure, and networks have to be dynamically and automatically reconfigurable to allow services to be set up quickly. With these trends in mind, it is impractical to perform detailed case-by-case simulations in order to predict and understand the behavior of such large-scale networks. Instead, one has to identify the key structural properties that affect network performance, reliability, and security. These structural properties can then be used to construct models that estimate network behavior in an efficient and scalable manner. A key observation regarding large-scale communications and biological and social networks has been the “small-world” property [1‐3]. More recent network models have focused on power-law degree distributions (PLDD) (for a few examples, see Refs. [4‐6]) as an explanation of or correlated with the small-world property. Evidence for PLDD has been found in data networks at the Internet protocol (IP) layer [7], for the worldwide web [4], and for the virtual network of social connections [8]. Although these features are interesting and important, the impact of intrinsic geometrical and topological features of large-scale networks on performance, reliability, and security is of much greater importance. Intuitively, it is known that traffic between nodes tends to go through a relatively small core of the network, as if the shortest path between them is curved inward. It has been suggested that this property may be due to global curvature or hyperbolicity of the network [9]. We present an analysis of the topological structure and static tolerance to errors and attacks of the September 2003 actualization of the Union for the Coordination of Transport of Electricity (UCTE) power grid, involving thirty-three different networks. Though every power grid studied has exponential degree distribution and most of them lack typical small-world topology, they display patterns of reaction to node loss similar to those observed in scale-free networks. We have found that the node removal behavior can be logarithmically related to the power grid size. This logarithmic behavior would suggest that, though size favors fragility, growth can reduce it. We conclude that, with the ever-growing demand for power and reliability, actual planning strategies to increase transmission systems would have to take into account this relative increase in vulnerability with size, in order to facilitate and improve the power grid design and functioning. Wireless networks, especially mobile ad hoc networks (MANET) and cognitive radio networks (CRN), are facing two new challenges beyond traditional random network model: opportunistic links with random nature due to fading and dynamic channel access, and dynamic network topology due to mobility and time varying. For a more general networking of Internet users as social complex networks, such challenges of further importance remain open. A generic and reliable model is therefore required to capture the information dissemination dynamics (IDD) of machine- to-machine communications or interactions in relation-based social networks. Inspired from epidemiology, we investigate the IDD in well-known complex networks by modified Susceptible-Infected (SI) model, which is surprisingly suitable for above multiple scenarios. Systematically categorizing such networks and examining conditions to adopt SI model for IDD in complex networks, the fundamental properties including mixing type, vertex connectivity and giant component size provide valid insights for quantitative analysis. We also investigate the IDD in networks with time-varying topology and show that all individuals receive the information in dynamic sense, even if the giant component size is not compatible with the number of individuals in static sense. Consequently, we successfully establish such analytical model for characterizing the IDD in complex networks consisting of opportunistic links and time-varying topology, feasible for various random wireless networks and social networks.