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Links 1 through 10 of 24 by Luca Bigliardi tagged network

We model a close-knit community of friends and enemies as a fully connected network with positive and negative signs on its edges. Theories from social psychology suggest that certain sign patterns are more stable than others. This notion of social "balance" allows us to define an energy landscape for such networks. Its structure is complex: numerical experiments reveal a landscape dimpled with local minima of widely varying energy levels. We derive rigorous bounds on the energies of these local minima and prove that they have a modular structure that can be used to classify them.

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The Internet infrastructure is severely stressed. Rapidly growing overheads associated with the primary function of the Internet—routing information packets between any two computers in the world—cause concerns among Internet experts that the existing Internet routing architecture may not sustain even another decade. In this paper, we present a method to map the Internet to a hyperbolic space. Guided by a constructed map, which we release with this paper, Internet routing exhibits scaling properties that are theoretically close to the best possible, thus resolving serious scaling limitations that the Internet faces today. Besides this immediate practical viability, our network mapping method can provide a different perspective on the community structure in complex networks.

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We establish concrete mathematical criteria to distinguish between different kinds of written storytelling, fictional and non-fictional. Specifically, we constructed a semantic network from both novels and news stories, with N independent words as vertices or nodes, and edges or links allotted to words occurring within m places of a given vertex; we call m the word distance. We then used measures from complex network theory to distinguish between news and fiction, studying the minimal text length needed as well as the optimized word distance m. The literature samples were found to be most effectively represented by their corresponding power laws over degree distribution P(k) and clustering coefficient C(k); we also studied the mean geodesic distance, and found all our texts were small-world networks. We observed a natural break-point at k=sqrt{N} where the power law in the degree distribution changed, leading to separate power law fit for the bulk and the tail of P(k).

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Every ten minutes the black box pings a server on the internet via the ethernet connection to check if it is for sale on the eBay. If its auction has ended or it has sold, it automatically creates a new auction of itself. If a person buys it on eBay, the current owner is required to send it to the new owner. The new owner must then plug it into ethernet, and the cycle repeats itself.

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Many mechanisms for the emergence and maintenance of altruistic behavior in social dilemma situations have been proposed. Indirect reciprocity is one such mechanism, where altruistic actions of a player are eventually rewarded by other players with whom the original player has not interacted. The upstream reciprocity is a type of indirect reciprocity and represents the concept that those helped by somebody will help other unspecified players. In the present study, we numerically investigate upstream reciprocity in heterogeneous contact networks. We show that heterogeneous networks considerably enhance cooperation in a game of upstream reciprocity. In heterogeneous networks, the most generous strategy, by which a player helps a neighbor on being helped and in addition initiates helping behavior, first occupies hubs in a network and then disseminates to other players.

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Operators of online social networks are increasingly sharing potentially
sensitive information about users and their relationships with advertisers,
application developers, and data-mining researchers. Privacy is typically
protected by anonymization, i.e., removing names, addresses, etc.
We present a framework for analyzing privacy and anonymity in social networks
and develop a new re-identification algorithm targeting anonymized social-
network graphs. To demonstrate its effectiveness on real- world networks, we
show that a third of the users who can be verified to have accounts on both
Twitter and Flickr can be re-identified in the anonymous Twitter graph with only
a 12% error rate.
Our de-anonymization algorithm is based purely on the network topology, does
not require creation of a large number of dummy “sybil” nodes, is robust to
noise and all existing defenses, and works even when the overlap between the
target network and the adversary’s auxiliary information is small.

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