23,966 research outputs found
Precursors and Laggards: An Analysis of Semantic Temporal Relationships on a Blog Network
We explore the hypothesis that it is possible to obtain information about the
dynamics of a blog network by analysing the temporal relationships between
blogs at a semantic level, and that this type of analysis adds to the knowledge
that can be extracted by studying the network only at the structural level of
URL links. We present an algorithm to automatically detect fine-grained
discussion topics, characterized by n-grams and time intervals. We then propose
a probabilistic model to estimate the temporal relationships that blogs have
with one another. We define the precursor score of blog A in relation to blog B
as the probability that A enters a new topic before B, discounting the effect
created by asymmetric posting rates. Network-level metrics of precursor and
laggard behavior are derived from these dyadic precursor score estimations.
This model is used to analyze a network of French political blogs. The scores
are compared to traditional link degree metrics. We obtain insights into the
dynamics of topic participation on this network, as well as the relationship
between precursor/laggard and linking behaviors. We validate and analyze
results with the help of an expert on the French blogosphere. Finally, we
propose possible applications to the improvement of search engine ranking
algorithms
Uncovering the Temporal Dynamics of Diffusion Networks
Time plays an essential role in the diffusion of information, influence and
disease over networks. In many cases we only observe when a node copies
information, makes a decision or becomes infected -- but the connectivity,
transmission rates between nodes and transmission sources are unknown.
Inferring the underlying dynamics is of outstanding interest since it enables
forecasting, influencing and retarding infections, broadly construed. To this
end, we model diffusion processes as discrete networks of continuous temporal
processes occurring at different rates. Given cascade data -- observed
infection times of nodes -- we infer the edges of the global diffusion network
and estimate the transmission rates of each edge that best explain the observed
data. The optimization problem is convex. The model naturally (without
heuristics) imposes sparse solutions and requires no parameter tuning. The
problem decouples into a collection of independent smaller problems, thus
scaling easily to networks on the order of hundreds of thousands of nodes.
Experiments on real and synthetic data show that our algorithm both recovers
the edges of diffusion networks and accurately estimates their transmission
rates from cascade data.Comment: To appear in the 28th International Conference on Machine Learning
(ICML), 2011. Website: http://www.stanford.edu/~manuelgr/netrate
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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