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Convex Programming
Information Network
Network Structure
Social Network
Synthetic Data
Maximum Likelihood
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On the Convexity of Latent Social Network Inference
On the Convexity of Latent Social Network Inference,Seth A. Myers,Jure Leskovec
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On the Convexity of Latent Social Network Inference
(
Citations: 5
)
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Seth A. Myers
,
Jure Leskovec
In many realworld scenarios, it is nearly impossible to collect explicit
social network
data. In such cases, whole networks must be inferred from underlying observations. Here, we formulate the problem of inferring latent social networks based on network diffusion or disease propagation data. We consider contagions propagating over the edges of an unobserved social network, where we only observe the times when nodes became infected, but not who infected them. Given such node infection times, we then identify the optimal network that best explains the observed data. We present a
maximum likelihood
approach based on
convex programming
with a l1like penalty term that encourages sparsity. Experiments on real and
synthetic data
reveal that our method nearperfectly recovers the underlying
network structure
as well as the parameters of the contagion propagation model. Moreover, our approach scales well as it can infer optimal networks of thousands of nodes in a matter of minutes.
Conference:
Neural Information Processing Systems  NIPS
, vol. abs/1010.5, 2010
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Citation Context
(3)
..., computer science
...
Bruce A. Desmarais
,
et al.
Statistical Inference for ValuedEdge Networks: The Generalized Expone...
...For example, the network inference problem [13,
24
] can be cast as a link prediction problem where no knowledge of the network is given...
Lars Backstrom
,
et al.
Supervised random walks: predicting and recommending links in social n...
...There are other works which are close to ours that also attempted to solve the similar problem by maximizing the likelihood [3,
13
], where the focus was on inferring the underlying network...
...In particular, [
13
] showed that the problem can effectively be transformed to a convex programming for which a global solution is guaranteed...
Kazumi Saito
,
et al.
Learning Diffusion Probability Based on Node Attributes in Social Netw...
References
(26)
Recovering timevarying networks of dependencies in social and biological studies
(
Citations: 16
)
Amr Ahmed
,
E. P. Xing
Journal:
Proceedings of The National Academy of Sciences  PNAS
, vol. 106, no. 29, pp. 1187811883, 2009
The mathematical theory of infectious diseases and its applications
(
Citations: 534
)
N. T. J. Bailey
Published in 1975.
Inferring relevant social networks from interpersonal communication
(
Citations: 6
)
Munmun De Choudhury
,
Winter A. Mason
,
Jake M. Hofman
,
Duncan J. Watts
Conference:
World Wide Web Conference Series  WWW
, pp. 301310, 2010
Sparse inverse covariance estimation with the graphical lasso
(
Citations: 158
)
JEROME FRIEDMAN
,
TREVOR HASTIE
,
ROBERT TIBSHIRANI
Journal:
Biostatistics
, vol. 9, no. 3, pp. 432441, 2007
Learning Probabilistic Models of Link Structure
(
Citations: 115
)
Lise Getoor
,
Nir Friedman
,
Daphne Koller
,
Benjamin Taskar
Journal:
Journal of Machine Learning Research  JMLR
, vol. 3, pp. 679707, 2002
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Citations
(5)
Statistical Inference for ValuedEdge Networks: The Generalized Exponential Random Graph Model
Bruce A. Desmarais
,
Skyler J. Cranmer
Journal:
PLOS One
, vol. 7, no. 1, 2012
Supervised random walks: predicting and recommending links in social networks
(
Citations: 8
)
Lars Backstrom
,
Jure Leskovec
Published in 2011.
Topology Discovery of Sparse Random Graphs With Few Participants
(
Citations: 1
)
Animashree Anandkumar
,
Avinatan Hassidim
,
Jonathan A. Kelner
Journal:
Sigmetrics Performance Evaluation Review  SIGMETRICS
, vol. abs/1102.5, pp. 253264, 2011
Social media analytics: tracking, modeling and predicting the flow of information through networks
Jure Leskovec
Conference:
World Wide Web Conference Series  WWW
, pp. 277278, 2011
Learning Diffusion Probability Based on Node Attributes in Social Networks
Kazumi Saito
,
Kouzou Ohara
,
Yuki Yamagishi
,
Masahiro Kimura
,
Hiroshi Motoda