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Links 1 through 6 of 6 by Luca Bigliardi tagged social

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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Revisit is a real-time visualization of the latest twitter messages (tweets) around a specific topic. Use it create your own twitter wall at a conference or an ambient display at your company or whatever other idea you come up with. In contrast to other twitterwalls, it provides a sense of the temporal dynamics in the twitter stream, and emphasizes the conversational threads established by retweets and @replies.

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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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