Paper Info
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, activity originates at certain nodes and subsequently spreads on the network through influence. In this work, we consider the problem of modeling the spread of influence and the identification of influential entities in a complex network when nodal activation can happen via two different mechanisms. The first mechanism of activation stems from factors that are intrinsic to the node. The second mechanism comes from the influence of connected neighbors. After introducing the model, we provide an algorithm to mine for the influential nodes in such a scenario by modifying the well-known influence maximization algorithm. We sketch a proof of the submodularity of the influence function under the new formulation and demonstrate the same on larger graphs. Based on the model, we explain how influential content creators can drive engagement on social media platforms. Using additional experiments on a Twitter dataset, we then show how the formulation can be applied to real-world social media datasets. Finally, we derive a centrality metric that takes into account both the mechanisms of activation and provides for an accurate, computationally efficient, alternate approach to the problem of identifying influencers under intrinsic activation.
Year
DOI
Venue
2018
10.1007/978-3-319-78196-9_6
Lecture Notes in Social Networks
Keywords
DocType
Volume
Complex networks,Influence maximization,Social influence,Intrinsic activation,Independent cascade,Monte carlo,Centrality,Spectral methods
Journal
abs/1707.05287
ISSN
Citations 
PageRank 
2190-5428
0
0.34
References 
Authors
20
4
Name
Order
Citations
PageRank
Arun V. Sathanur100.34
Mahantesh Halappanavar221833.64
Yi Shi32110120.50
Walin Sagduyu400.34