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Learning with sparsity for detecting...

Wang, Yingze.

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Learning with sparsity for detecting influential nodes in implicit information diffusion networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning with sparsity for detecting influential nodes in implicit information diffusion networks./
Author:	Wang, Yingze.
Description:	135 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
Contained By:	Dissertation Abstracts International76-01B(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3582636
ISBN:	9781321209723

Learning with sparsity for detecting influential nodes in implicit information diffusion networks.
Wang, Yingze.

Learning with sparsity for detecting influential nodes in implicit information diffusion networks. - 135 p.

Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2014.

This item must not be sold to any third party vendors.

The diffusion of information and spreading in uence are ubiquitous in social networks. How to model and extract useful information from diffusion networks especially in social media domain is still an open research area that requires significant attention. Many real applications pose new challenges in modeling information diffusion process. In particular, the first challenge comes from the fact that the underlying network structure over which the propagation spreads is unknown or unobserved. It is often the case that one can only observes that when nodes got infected by which contagion but without the knowledge about who infecting whom. The second challenge comes from the simultaneous transmissions of multiple correlated contagions through an implicit network. The third one comes from strong temporal effect in the diffusion process which needs to be carefully modeled.

ISBN: 9781321209723Subjects--Topical Terms:

626642
Computer Science.

Learning with sparsity for detecting influential nodes in implicit information diffusion networks.
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020 $a 9781321209723
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100 1 $a Wang, Yingze. $3 3169935
245 1 0 $a Learning with sparsity for detecting influential nodes in implicit information diffusion networks.
300 $a 135 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
500 $a Adviser: Shi-Kuo Chang.
502 $a Thesis (Ph.D.)--University of Pittsburgh, 2014.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a The diffusion of information and spreading in uence are ubiquitous in social networks. How to model and extract useful information from diffusion networks especially in social media domain is still an open research area that requires significant attention. Many real applications pose new challenges in modeling information diffusion process. In particular, the first challenge comes from the fact that the underlying network structure over which the propagation spreads is unknown or unobserved. It is often the case that one can only observes that when nodes got infected by which contagion but without the knowledge about who infecting whom. The second challenge comes from the simultaneous transmissions of multiple correlated contagions through an implicit network. The third one comes from strong temporal effect in the diffusion process which needs to be carefully modeled.
520 $a In my thesis, we address two fundamental tasks, forecasting and in uential-node detection, in an implicit diffusion network by a unified approach. In particular, we first proposed a sparse linear in uence model (SLIM) which takes a nice form of a convex optimization problem. We further extended SLIM to multi-task sparse linear in uence model (MSLIM), which could model diffusion networks with multiple correlated contagions. MSLIM, as a richer model than SLIM, not only improves prediction accuracy, but also allows to select in uential nodes on a finer grid, i.e., select different sets of in uential nodes for different contagions. For SLIM and MSLIM, we developed both deterministic and stochastic optimization algorithms for solving the corresponding problems and showed the fast theoretical convergence guarantees.
590 $a School code: 0178.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Computer. $3 1669061
650 4 $a Engineering, General. $3 1020744
690 $a 0984
690 $a 0464
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710 2 $a University of Pittsburgh. $3 958527
773 0 $t Dissertation Abstracts International $g 76-01B(E).
790 $a 0178
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3582636