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Clustering, assortativity, and communities are key features of complex networks. We probe dependencies between these features and find that ensembles of networks with high clustering display both high assortativity by degree and prominent community structure, while ensembles with high assortativity show much less enhancement of the clustering or community(More)
For many real-world networks only a small "sampled" version of the original network may be investigated; those results are then used to draw conclusions about the actual system. Variants of breadth-first search (BFS) sampling, which are based on epidemic processes, are widely used. Although it is well established that BFS sampling fails, in most cases, to(More)
Directed networks are ubiquitous and are necessary to represent complex systems with asymmetric interactions--from food webs to the World Wide Web. Despite the importance of edge direction for detecting local and community structure, it has been disregarded in studying a basic type of global diversity in networks: the tendency of nodes with similar numbers(More)
A new heuristic based on vertex invariants is developed to rapidly distinguish non-isomorphic graphs to a desired level of accuracy. The method is applied to sample subgraphs from an E.coli protein interaction network, and as a probe for discovery of extended motifs. The network's structure is described using statistical properties of its N-node subgraphs(More)
The basic laws of physics are simple, so why is the world complex? The theory of self-organized criticality posits that complex behavior in nature emerges from the dynamics of extended, dissipative systems that evolve through a sequence of meta-stable states into a critical state, with long range spatial and temporal correlations. Minor disturbances lead to(More)
The simplest null models for networks, used to distinguish significant features of a particular network from a priori expected features, are random ensembles with the degree sequence fixed by the specific network of interest. These "fixed degree sequence" (FDS) ensembles are, however, famously resistant to analytic attack. In this paper we introduce(More)
We study networks representing the dynamics of elementary 1D cellular automata (CA) on finite lattices. We analyze scaling behaviors of both local and global network properties as a function of system size. The scaling of the largest node in-degree is obtained analytically for a variety of CA including rules 22, 54, and 110. We further define the path(More)
We propose a metric to quantify correlations between earthquakes. The metric consists of a product involving the time interval and spatial distance between two events, as well as the magnitude of the first one. According to this metric, events typically are strongly correlated to only one or a few preceding ones. Thus a classification of events as(More)
We discuss potential market mechanisms for the GRID. A complete dynamical model of a GRID market is defined with three types of agents. Providers, middlemen and users exchange universal GRID computing units (GCUs) at varying prices. Providers and middlemen have strategies aimed at maximizing profit while users are 'satisficing' agents, and only change their(More)