640 research outputs found
Local Search in Unstructured Networks
We review a number of message-passing algorithms that can be used to search
through power-law networks. Most of these algorithms are meant to be
improvements for peer-to-peer file sharing systems, and some may also shed some
light on how unstructured social networks with certain topologies might
function relatively efficiently with local information. Like the networks that
they are designed for, these algorithms are completely decentralized, and they
exploit the power-law link distribution in the node degree. We demonstrate that
some of these search algorithms can work well on real Gnutella networks, scale
sub-linearly with the number of nodes, and may help reduce the network search
traffic that tends to cripple such networks.Comment: v2 includes minor revisions: corrections to Fig. 8's caption and
references. 23 pages, 10 figures, a review of local search strategies in
unstructured networks, a contribution to `Handbook of Graphs and Networks:
From the Genome to the Internet', eds. S. Bornholdt and H.G. Schuster
(Wiley-VCH, Berlin, 2002), to be publishe
Information Flow in Social Groups
We present a study of information flow that takes into account the
observation that an item relevant to one person is more likely to be of
interest to individuals in the same social circle than those outside of it.
This is due to the fact that the similarity of node attributes in social
networks decreases as a function of the graph distance. An epidemic model on a
scale-free network with this property has a finite threshold, implying that the
spread of information is limited. We tested our predictions by measuring the
spread of messages in an organization and also by numerical experiments that
take into consideration the organizational distance among individuals
Can Cascades be Predicted?
On many social networking web sites such as Facebook and Twitter, resharing
or reposting functionality allows users to share others' content with their own
friends or followers. As content is reshared from user to user, large cascades
of reshares can form. While a growing body of research has focused on analyzing
and characterizing such cascades, a recent, parallel line of work has argued
that the future trajectory of a cascade may be inherently unpredictable. In
this work, we develop a framework for addressing cascade prediction problems.
On a large sample of photo reshare cascades on Facebook, we find strong
performance in predicting whether a cascade will continue to grow in the
future. We find that the relative growth of a cascade becomes more predictable
as we observe more of its reshares, that temporal and structural features are
key predictors of cascade size, and that initially, breadth, rather than depth
in a cascade is a better indicator of larger cascades. This prediction
performance is robust in the sense that multiple distinct classes of features
all achieve similar performance. We also discover that temporal features are
predictive of a cascade's eventual shape. Observing independent cascades of the
same content, we find that while these cascades differ greatly in size, we are
still able to predict which ends up the largest
Networks of strong ties
Social networks transmitting covert or sensitive information cannot use all
ties for this purpose. Rather, they can only use a subset of ties that are
strong enough to be ``trusted''. In this paper we consider transitivity as
evidence of strong ties, requiring that each tie can only be used if the
individuals on either end also share at least one other contact in common. We
examine the effect of removing all non-transitive ties in two real social
network data sets. We observe that although some individuals become
disconnected, a giant connected component remains, with an average shortest
path only slightly longer than that of the original network. We also evaluate
the cost of forming transitive ties by deriving the conditions for the
emergence and the size of the giant component in a random graph composed
entirely of closed triads and the equivalent Erdos-Renyi random graph.Comment: 10 pages, 7 figure
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