Mining Information Spreading Based on Users' Retweet Behavior in Twitter

2013 ◽  
Vol 380-384 ◽  
pp. 2866-2870 ◽  
Author(s):  
Rong Ze Xia ◽  
Yan Jia ◽  
Wang Qun Lin ◽  
Hu Li
Keyword(s):  
Social Networks ◽  
Power Law ◽  
Power Law Distribution ◽  
Spreading Process ◽  
Spreading Model ◽  
The World ◽  
Information Spread ◽  
Information Spreading

Twitter is one of the largest social networks in the world. People could share contents on it. When we interact with each other, the information spreads. And its users retweet behavior that makes information spread so fast. So there comes an important question: Whats about users retweet behavior? Could we simulate information spreading in twitter by retweeting behavior? We crawl twitter and mine information spreading based on users retweet behavior in it. Through our dateset, we verify the power-law distribution of the retweet-width and retweet-depth. At the same time, we study the correlation between retweet-width and retweet-depth. Finally, we propose an information spreading model to simulate the information spreading process in twitter and get a good result.

Priority Weighted Fitness Model in Networks

2012 ◽  
Vol 229-231 ◽  
pp. 1854-1857
Author(s):  
Xin Yi Chen
Keyword(s):  
World Wide Web ◽  
Power Law ◽  
Common Property ◽  
World Wide ◽  
Genetic Networks ◽  
Power Law Distribution ◽  
Large Networks ◽  
Scale Free ◽  
The World ◽  
Complex Topology

Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a power-law distribution. This feature was found to be a consequence of three generic mechanisms: (i) networks expand continuously by the addition of new vertices, (ii) new vertex with priority selected different edges of weighted selected that connected to different vertices in the system, and (iii) by the fitness probability that a new vertices attach preferentially to sites that are already well connected. A model based on these ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena. Experiment results show that the model is more close to the actual Internet network.

Information spreading on metapopulation networks with heterogeneous contacting

2021 ◽  
Author(s):  
Yanyi Nie ◽  
Liming Pan ◽  
Tao Lin ◽  
Wei Wang
Keyword(s):  
Normal Distribution ◽  
Exponential Distribution ◽  
Power Law ◽  
Social Systems ◽  
Real Data ◽  
Power Law Distribution ◽  
Specific Distribution ◽  
Mean And Variance ◽  
Information Spreading ◽  
Individual Contact

Extensive real-data reveals that individuals exhibit heterogeneous contacting frequency in social systems. We propose a mathematical model to investigate the effects of heterogeneous contacting for information spreading in metapopulation networks. In the proposed model, we assume the number of contacting (NOC) distribution follows a specific distribution, including the normal, exponential, and power-law distributions. We utilize the Markov chain method to study the information spreading dynamics and find that mean and variance display no significant effect on the outbreak threshold for all the considered distributions. Under the same values of NOC distribution’s mean and variance, the information prevalence is largest when the distribution of NOC follows the normal distribution and second-largest for the exponential distribution, the smallest for the power-law distribution. When the distribution of NOC obeys the normal distribution, experimental results show that the information prevalence will decrease with individual contact ability heterogeneity. We observe similar phenomena when the distribution of NOC follows a power-law and exponential distribution. Furthermore, a larger mean of individual contact capacity distribution will result in higher information prevalence.

scholarly journals Access to news on line: myths, risks and facts

2011 ◽  
Vol 10 (02) ◽  
pp. C03
Author(s):  
Lella Mazzoli
Keyword(s):  
Social Networks ◽  
Daily Practice ◽  
The Internet ◽  
Spreading Process ◽  
The Past ◽  
Main Medium ◽  
On Line ◽  
Information Spreading

Although the debates on the Internet (sceptical, enthusiastic and finally more mature ones) in our country started in the mid 90s, it is only over the past few years that the Internet, especially thanks to social networks, has become a daily practice for millions of Italians. Television still is the main medium to spread information, but as it becomes increasingly cross-bred with the Internet (and other media too), the information-spreading process deeply changes. This creates, also in our country, the preconditions for the development of a web public (an active and connected one), founded on the new practices of multitasking and participatory information.

scholarly journals Using Power-Law Degree Distribution to Accelerate PageRank

2012 ◽  
Vol 1 (2) ◽  
pp. 63-70
Author(s):  
Zhaoyan Jin ◽  
Quanyuan Wu
Keyword(s):  
World Wide Web ◽  
Power Law ◽  
Real World ◽  
Degree Distribution ◽  
World Wide ◽  
Power Law Distribution ◽  
The World ◽  
Real World Applications ◽  
Real World Datasets ◽  
Law Degree

The PageRank vector of a network is very important, for it can reflect the importance of a Web page in the World Wide Web, or of a people in a social network. However, with the growth of the World Wide Web and social networks, it needs more and more time to compute the PageRank vector of a network. In many real-world applications, the degree and PageRank distributions of these complex networks conform to the Power-Law distribution. This paper utilizes the degree distribution of a network to initialize its PageRank vector, and presents a Power-Law degree distribution accelerating algorithm of PageRank computation. Experiments on four real-world datasets show that the proposed algorithm converges more quickly than the original PageRank algorithm.DOI: 10.18495/comengapp.12.063070

Correlating international inflowing and outflowing remittances and migrants

2020 ◽  
Vol 31 (03) ◽  
pp. 2050047
Author(s):  
J. Hernández-Casildo ◽  
M. del Castillo-Mussot ◽  
E. Hernández-Ramirez ◽  
L. Guzmán-Vargas
Keyword(s):  
World Bank ◽  
Power Law ◽  
Statistical Correlation ◽  
Power Law Distribution ◽  
Economic Phenomenon ◽  
International Remittances ◽  
The World ◽  
And Migration

Remittances, as money or goods that people send to families and friends, are very important social and economic phenomenon at local, national, regional and international levels. In the year 2017, total international remittances were at levels around USD 613 billion. From World Bank bilateral remittances and migration matrixes, we calculate for each country and territory its aggregated or total amount of remittances inflow (TRI) coming from the rest of the world, its total remittances outflow (TRO) extracted from that country and sent to all other countries, its total emigrant stock (TEMI) living overseas, and its total number of foreign-world immigrants (TIMM) living in that country. For each of these quantities, its highest-ranked countries follow an approximate Pareto power law distribution. Remittances and migrants flow in opposite directions, the statistical correlation [Formula: see text] between TRI and TEMI is 0.79, and between TRO and TIMM is 0.97. Both inflowing remittances per emigrant TRI/TEMI and outflowing remittances per immigrant TRO/TIMM fluctuate approximately around 3100 USD per year.

Scalable Method for Information Spread Control in Social Networks

2018 ◽  
pp. 220-240
Author(s):  
Michal Wojtasiewicz ◽  
Mieczysław Kłopotek
Keyword(s):  
Social Networks ◽  
Cluster Analysis ◽  
Random Walks ◽  
Signal Propagation ◽  
Control Signal ◽  
Information Spread ◽  
Information Spreading

In this chapter, scalable and parallelized method for cluster analysis based on random walks is presented. The aim of the algorithm introduced in this chapter is to detect dense sub graphs (clusters) and sparse sub graphs (bridges) which are responsible for information spreading among found clusters. The algorithm is sensitive to the uncertainty involved in assignment of vertices. It distinguishes groups of nodes that form sparse clusters. These groups are mostly located in places crucial for information spreading so one can control signal propagation between separated dense sub graphs by using algorithm provided in this work. Authors have also proposed new coefficient which measures quality of given clustering with respect to information spread control between clusters. Measures presented in this paper can be used for determining quality of whole partitioning or a single bridge.

Activity Sequence Generation Using Universal Mobility Patterns

2021 ◽  
pp. 036119812110624
Author(s):  
Wim Ectors ◽  
Bruno Kochan ◽  
Davy Janssens ◽  
Tom Bellemans ◽  
Geert Wets
Keyword(s):  
Power Law ◽  
Sequential Sampling ◽  
Model Development ◽  
Generation Model ◽  
Mobility Patterns ◽  
Power Law Distribution ◽  
Sequence Generation ◽  
Activity Sequence ◽  
Basic Set ◽  
The World

Previous work has established that rank ordered single-day activity sequences from various study areas exhibit a universal power law distribution called Zipf’s law. By analyzing datasets from across the world, evidence was provided that it is in fact a universal distribution. This study focuses on a potential mechanism that leads to the power law distribution that was previously discovered. It makes use of 15 household travel survey (HTS) datasets from study areas all over the world to demonstrate that reasonably accurate sets of activity sequences (or “schedules”) can be generated with extremely little information required; the model requires no input data and contains few tunable parameters. The activity sequence generation mechanism is based on sequential sampling from two universal distributions: (i) the distributions of the number of activities (trips) and (ii) the activity types (trip purposes). This paper also attempts to demonstrate the universal nature of these distributions by fitting several equations to the 15 HTS datasets. The lightweight activity sequence generation model can be implemented in any (lightweight) transportation model to create a basic set of activity sequences, saving effort and cost in data collection and in model development and calibration.

Power-Law Distribution of the World Wide Web

Science ◽  
2000 ◽  
Vol 287 (5461) ◽  
pp. 2115 ◽  
Author(s):  
Lada A. Adamic ◽  
Bernardo A. Huberman ◽  
A.-L. Barabási ◽  
R. Albert ◽  
H. Jeong ◽  
...  
Keyword(s):  
World Wide Web ◽  
Power Law ◽  
World Wide ◽  
Power Law Distribution ◽  
The World
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