Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Asynchronous-Time Information Diffusion Models and its
  Application to Behavioral Data Analysis over Social Networks   One of the interesting and important problems of information diffusion over a
large social network is to identify an appropriate model from a limited amount
of diffusion information. There are two contrasting approaches to model
information diffusion: a push type model known as Independent Cascade (IC)
model and a pull type model known as Linear Threshold (LT) model. We extend
these two models (called AsIC and AsLT in this paper) to incorporate
asynchronous time delay and investigate 1) how they differ from or similar to
each other in terms of information diffusion, 2) whether the model itself is
learnable or not from the observed information diffusion data, and 3) which
model is more appropriate to explain for a particular topic (information) to
diffuse/propagate. We first show there can be variations with respect to how
the time delay is modeled, and derive the likelihood of the observed data being
generated for each model. Using one particular time delay model, we show the
model parameters are learnable from a limited amount of observation. We then
propose a method based on predictive accuracy by which to select a model which
better explains the observed data. Extensive evaluations were performed. We
first show using synthetic data with the network structures taken from real
networks that there are considerable behavioral differences between the AsIC
and the AsLT models, the proposed methods accurately and stably learn the model
parameters, and identify the correct diffusion model from a limited amount of
observation data. We next apply these methods to behavioral analysis of topic
propagation using the real blog propagation data, and show there is a clear
indication as to which topic better follows which model although the results
are rather insensitive to the model selected at the level of discussing how far
and fast each topic propagates from the learned parameter values.

22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
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The categories of this paper are: cmp-lg
2
The categories of this paper are: math
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga