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Title: Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks

Abstract

In many complex networked systems such as online social networks, at any given time, activity originates at certain nodes and subsequently spreads on the network through influence. To model the spread of influence in such a scenario, we consider the problem of identification of influential entities in a complex network when nodal activation can happen through two different mechanisms. The first mode of activation is due mechanisms intrinsic to the node. The second mechanism is through the influence of connected neighbors. In this work, we present a simple probabilistic formulation that models such self-evolving systems where information diffusion occurs primarily because of the intrinsic activity of users and the spread of activity occurs due to influence. We provide an algorithm to mine for the influential seeds in such a scenario by modifying the well-known influence maximization framework with the independent cascade diffusion model. We provide small motivating examples to provide an intuitive understanding of the effect of including the intrinsic activation mechanism. We sketch a proof of the submodularity of the influence function under the new formulation and demonstrate the same with larger graphs. We then show by means of additional experiments on a real-world twitter dataset how the formulationmore » can be applied to real-world social media datasets. Finally we derive a computationally efficient centrality metric that takes into account, both the mechanisms of activation and provides for an accurate as well as computationally efficient alternative approach to the problem of identifying influencers under intrinsic activation.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1455315
Report Number(s):
PNNL-SA-122958
453040300
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Book
Resource Relation:
Related Information: Social Network Based Big Data Analysis and Applications. Lecture Notes in Social Networks, 123-142
Country of Publication:
United States
Language:
English

Citation Formats

Visweswara Sathanur, Arun, Halappanavar, Mahantesh, Shi, Yi, and Sagduyu, Yalin. Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks. United States: N. p., 2018. Web. doi:10.1007/978-3-319-78196-9_6.
Visweswara Sathanur, Arun, Halappanavar, Mahantesh, Shi, Yi, & Sagduyu, Yalin. Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks. United States. doi:10.1007/978-3-319-78196-9_6.
Visweswara Sathanur, Arun, Halappanavar, Mahantesh, Shi, Yi, and Sagduyu, Yalin. Mon . "Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks". United States. doi:10.1007/978-3-319-78196-9_6.
@article{osti_1455315,
title = {Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks},
author = {Visweswara Sathanur, Arun and Halappanavar, Mahantesh and Shi, Yi and Sagduyu, Yalin},
abstractNote = {In many complex networked systems such as online social networks, at any given time, activity originates at certain nodes and subsequently spreads on the network through influence. To model the spread of influence in such a scenario, we consider the problem of identification of influential entities in a complex network when nodal activation can happen through two different mechanisms. The first mode of activation is due mechanisms intrinsic to the node. The second mechanism is through the influence of connected neighbors. In this work, we present a simple probabilistic formulation that models such self-evolving systems where information diffusion occurs primarily because of the intrinsic activity of users and the spread of activity occurs due to influence. We provide an algorithm to mine for the influential seeds in such a scenario by modifying the well-known influence maximization framework with the independent cascade diffusion model. We provide small motivating examples to provide an intuitive understanding of the effect of including the intrinsic activation mechanism. We sketch a proof of the submodularity of the influence function under the new formulation and demonstrate the same with larger graphs. We then show by means of additional experiments on a real-world twitter dataset how the formulation can be applied to real-world social media datasets. Finally we derive a computationally efficient centrality metric that takes into account, both the mechanisms of activation and provides for an accurate as well as computationally efficient alternative approach to the problem of identifying influencers under intrinsic activation.},
doi = {10.1007/978-3-319-78196-9_6},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon May 21 00:00:00 EDT 2018},
month = {Mon May 21 00:00:00 EDT 2018}
}

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