The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices

We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity ” over possible

Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled

In a cell or microorganism the processes that generate mass, energy, information transfer, and cell fate specification are seamlessly integrated through a complex network of various cellular constituents and reactions. However, despite the key role these networks play in sustaining various cellular

Nobody wants to use technology to recreate education as it is, yet there is not much to distinguish what goes on in most computer-supported classrooms versus traditional classrooms. Kay (1991) has suggested that the phenomenon of reframing innovations to recreate the familiar is itself commonplace. Thus, one sees all manner of powerful technology (Hypercard, CD-ROM, Lego Logo, and so forth) used to conduct shopworn school activities: copying material from one resource into another (e.g., using Hypercard to assemble sound and visual bites produced by others), and following step-by-step procedures (e.g., creating Lego Logo machines by following steps in a manual). With new technologies, student-generated collages and reproductions appear more inventive and sophisticated-with impressive displays of sound, video, and typography-but from a cognitive perspective, it is not clear what, if any, knowledge content has been processed by the students. In this chapter we offer a suggestion for how to escape the pattern of reinventing the familiar with educational technology. Knowledge-building discourse is at the heart of the superior education that we have in mind. We argue that the classroom needs to foster

The Internet, as well as many other networks, has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from

restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored representations of probability distributions that generalize the naive Bayesian classifier and explicitly

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In this paper, we address the problem of routing in intermittently connected networks. In such networks there is no guarantee that a fully connected path between source and destination exists at any time, rendering traditional routing protocols unable to deliver messages between hosts

Active networks are a novel approach to network architecture in which the switches of the network perform customized computations on the messages flowing through them. This approach is motivated by both lead user applications, which perform user-driven computation at nodes within the network today