183,340 research outputs found
Effects of Network Communities and Topology Changes in Message-Passing Computation of Harmonic Influence in Social Networks
The harmonic influence is a measure of the importance of nodes in social
networks, which can be approximately computed by a distributed message-passing
algorithm. In this extended abstract we look at two open questions about this
algorithm. How does it perform on real social networks, which have complex
topologies structured in communities? How does it perform when the network
topology changes while the algorithm is running? We answer these two questions
by numerical experiments on a Facebook ego network and on synthetic networks,
respectively. We find out that communities can introduce artefacts in the final
approximation and cause the algorithm to overestimate the importance of "local
leaders" within communities. We also observe that the algorithm is able to
adapt smoothly to changes in the topology.Comment: 4 pages, 7 figures, submitted as conference extended abstrac
Searching for superspreaders of information in real-world social media
A number of predictors have been suggested to detect the most influential
spreaders of information in online social media across various domains such as
Twitter or Facebook. In particular, degree, PageRank, k-core and other
centralities have been adopted to rank the spreading capability of users in
information dissemination media. So far, validation of the proposed predictors
has been done by simulating the spreading dynamics rather than following real
information flow in social networks. Consequently, only model-dependent
contradictory results have been achieved so far for the best predictor. Here,
we address this issue directly. We search for influential spreaders by
following the real spreading dynamics in a wide range of networks. We find that
the widely-used degree and PageRank fail in ranking users' influence. We find
that the best spreaders are consistently located in the k-core across
dissimilar social platforms such as Twitter, Facebook, Livejournal and
scientific publishing in the American Physical Society. Furthermore, when the
complete global network structure is unavailable, we find that the sum of the
nearest neighbors' degree is a reliable local proxy for user's influence. Our
analysis provides practical instructions for optimal design of strategies for
"viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure
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