Structural properties and generative model of non-giant connected components in social networks

Niu, Jianwei; Wang, Lei

doi:10.1007/s11432-015-0790-x

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…In this section, we analyze the structure of the optimal networks to determine the effect of optimizing APL on different networks. The assortative index r and cluster coefficient c are important indices for measuring network characteristics 50 . The assortative index r measures the characteristic of assortative (or disassortative) mixing, and can be formulated as Eq.…”

Section: Resultsmentioning

confidence: 99%

Network structure optimization for social networks by minimizing the average path length

Wei

2022

Computing

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Network structure plays an important role in the natural and social sciences. Optimization of network structure in achieving specified goals has been a major research focus. In this paper, we propose a definition of structural optimization in terms of minimizing the network's average path length (APL) by adding edges. We suggest a memetic algorithm to find the minimum-APL solution by adding edges. Experiments show that the proposed algorithm can solve this problem efficiently. Further, we find that APL will ultimately decrease linearly in the process of adding edges, which is affected by the network diameter.

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Section: Resultsmentioning

confidence: 99%

Network structure optimization for social networks by minimizing the average path length

Wei

2022

Computing

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“…In recent years, with the rapid development and demanding requirements of online social networks [1], [2] (e.g., Twitter, Facebook, Flickr), tremendous interests have arisen from the study of information diffusion. An example of information diffusion is: When someone adopts a piece of information, his or her neighbors may be influenced and then consider adopting the same information.…”

Section: Introductionmentioning

confidence: 99%

Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction

Dong

Zhang

Sun

et al. 2017

IEEE Trans. Knowl. Data Eng.

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Abstract-Modeling the process of information diffusion is a challenging problem. Although numerous attempts have been made in order to solve this problem, very few studies are actually able to simulate and predict temporal dynamics of the diffusion process. In this paper, we propose a novel information diffusion model, namely GT model, which treats the nodes of a network as intelligent and rational agents and then calculates their corresponding payoffs, given different choices to make strategic decisions. By introducing time-related payoffs based on the diffusion data, the proposed GT model can be used to predict whether or not the user's behaviors will occur in a specific time interval. The user's payoff can be divided into two parts: social payoff from the user's social contacts and preference payoff from the user's idiosyncratic preference. We here exploit the global influence of the user and the social influence between any two users to accurately calculate the social payoff. In addition, we develop a new method of presenting social influence that can fully capture the temporal dynamics of social influence. Experimental results from two different datasets, Sina Weibo and Flickr, demonstrate the rationality and effectiveness of the proposed prediction method with different evaluation metrics.

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New Data Representation and Simulate Over Social Media Using Bayes Classifier of Machine Learning

Yallamati,

Buradagunta,

Almas

et al. 2023

Lecture Notes in Electrical Engineering

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Structural properties and generative model of non-giant connected components in social networks

Cited by 7 publications

References 16 publications

Network structure optimization for social networks by minimizing the average path length

Network structure optimization for social networks by minimizing the average path length

Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction

New Data Representation and Simulate Over Social Media Using Bayes Classifier of Machine Learning

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