# IEEE Transactions on Network Science and Engineering

## Volume 4 Issue 2 • April-June 1 2017

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## Filter Results

Displaying Results 1 - 8 of 8
• ### State of the Journal Editorial

Publication Year: 2017, Page(s):72 - 73
| PDF (183 KB) | HTML
• ### Message from the Incoming Editor-in-Chief

Publication Year: 2017, Page(s): 74
| PDF (30 KB) | HTML
• ### A Test of Hypotheses for Random Graph Distributions Built from EEG Data

Publication Year: 2017, Page(s):75 - 82
| | PDF (571 KB) | HTML

The theory of random graphs has been applied in recent years to model neural interactions in the brain. While the probabilistic properties of random graphs has been extensively studied, the development of statistical inference methods for this class of objects has received less attention. In this work we propose a non-parametric test of hypotheses to test if a sample of random graphs was generated... View full abstract»

• ### Competitive Propagation: Models, Asymptotic Behavior and Quality-Seeding Games

Publication Year: 2017, Page(s):83 - 99
Cited by:  Papers (1)
| | PDF (863 KB) | HTML

In this paper we propose a class of propagation models for multiple competing products over a social network. We consider two propagation mechanisms: social conversion and self conversion, corresponding, respectively, to endogenous and exogenous factors. A novel concept, the product-conversion graph, is proposed to characterize the interplay among competing products. According to the chronological... View full abstract»

• ### Diffusion of Innovation in Large Scale Graphs

Publication Year: 2017, Page(s):100 - 111
| | PDF (439 KB) | HTML

Will a new smartphone application diffuse deeply in the population or will it sink into oblivion soon? To predict this, we argue that common models of spread of innovations based on cascade dynamics or epidemics may not be fully adequate. In this paper, we model the spread of a new technological item in a population through a novel network dynamics where diffusion is based on the word-of-mouth and... View full abstract»

• ### Fast Generation of Spatially Embedded Random Networks

Publication Year: 2017, Page(s):112 - 119
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Spatially Embedded Random Networks such as the Waxman random graph have been used in many settings for synthesizing networks. Prior to our work, there existed no software for generating these efficiently. Existing techniques are $O(n^2)$ where ... View full abstract»

• ### Information Cascades in Feed-Based Networks of Users with Limited Attention

Publication Year: 2017, Page(s):120 - 128
Cited by:  Papers (1)
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We build a model of information cascades on feed-based networks, taking into account the finite attention span of users, message generation rates and message forwarding rates. Through simulation of this model, we study the effect of the extent of user attention on the probability that the cascade becomes viral. In analogy with a branching process, we estimate the branching factor associated with t... View full abstract»

Publication Year: 2017, Page(s):129 - 139
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Large-scale cascading failures can be triggered by very few initial failures, leading to severe damages in complex networks. This paper studies load-dependent cascading failures in random networks consisting of a large but finite number of components. Under a random single-node attack, a framework is developed to quantify the damage at each stage of a cascade. Estimations and analyses for the frac... View full abstract»

## Aims & Scope

The IEEE Transactions on Network Science and Engineering is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Dapeng Oliver Wu

University of Florida

Dept. of Electrical  &  Computer Engineering

P. O. Box 116130

Gainesville, FL 32611

Email: dpwu@ufl.edu