Modeling Information Diffusion via Reputation Estimation

2016 ◽  
pp. 136-150 ◽  
Author(s):  
Bao-Thien Hoang ◽  
Kamel Chelghoum ◽  
Imed Kacem
Keyword(s):  
Information Diffusion

An Information Diffusion Based Recommendation Framework for Micro-Blogging

2010 ◽  
Author(s):  
Jiesi Cheng ◽  
Aaron R. Sun ◽  
Daning Hu ◽  
Daniel Dajun Zeng
Keyword(s):  
Information Diffusion

Studying Populations of Online Communities

2019 ◽  
pp. 173-193
Author(s):  
Benjamin Mako Hill ◽  
Aaron Shaw
Keyword(s):  
Online Communities ◽  
Information Diffusion ◽  
Diffusion Processes ◽  
Empirical Studies ◽  
Population Level ◽  
Study Groups ◽  
Population Based ◽  
Large Majority ◽  
Community Processes ◽  
Published Research

While the large majority of published research on online communities consists of analyses conducted entirely within individual communities, this chapter argues for a population-based approach, in which researchers study groups of similar communities. For example, although there have been thousands of papers published about Wikipedia, a population-based approach might compare all wikis on a particular topic. Using examples from published empirical studies, the chapter describes five key benefits of this approach. First, it argues that population-level research increases the generalizability of findings. Next, it describes four processes and dynamics that are only possible to study using populations: community-level variables, information diffusion processes across communities, ecological dynamics, and multilevel community processes. The chapter concludes with a discussion of a series of limitations and challenges.

scholarly journals Impact of information diffusion on epidemic spreading in partially mapping two-layered time-varying networks

2021 ◽  
Vol 105 (4) ◽  
pp. 3819-3833
Author(s):  
Haili Guo ◽  
Qian Yin ◽  
Chengyi Xia ◽  
Matthias Dehmer
Keyword(s):  
Information Diffusion ◽  
Time Varying ◽  
Epidemic Spreading ◽  
Time Varying Networks

A microbial treatment population dynamics optimization approach

2021 ◽  
pp. 1-15
Author(s):  
Jinding Gao
Keyword(s):  
Population Dynamics ◽  
Information Exchange ◽  
Information Diffusion ◽  
Optimization Problems ◽  
Microbial Control ◽  
Function Optimization ◽  
Optimization Approach ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

scholarly journals A Survey of Information Cascade Analysis

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Fan Zhou ◽  
Xovee Xu ◽  
Goce Trajcevski ◽  
Kunpeng Zhang
Keyword(s):  
Information Diffusion ◽  
Research Work ◽  
Viral Marketing ◽  
Graph Representation ◽  
Digital Information ◽  
Information Cascades ◽  
Challenges And Opportunities ◽  
Scientific Papers ◽  
Popularity Prediction ◽  
Types Of Information

The deluge of digital information in our daily life—from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising—offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better understanding of the mechanisms driving the spread of information and quantifying the outcome of information diffusion. This article presents a comprehensive review and categorization of information popularity prediction methods, from feature engineering and stochastic processes , through graph representation , to deep learning-based approaches . Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. We then present a taxonomy that categorizes existing works into the aforementioned three main groups as well as the main subclasses in each group, and we systematically review cutting-edge research work. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities in this field.

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