Information diffusion in online social networks: a survey

By Adrien Guille, Hakim Hacid, Cécile Favre, Djamel Abdelkader Zighed. SGMD, 2013

916 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/4a3df0c64d4e786c59334d4ea54c99b1808760a7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Sociology and Political Science

1. Kai Shu
2. Huan Liu
3. Suhang Wang
4. Xinyi Zhou
5. Jiliang Tang
6. Jian Pei
7. Shixia Liu
8. Yingcai Wu

In Statistical and Nonlinear Physics

1. Peng Cui
2. Xiao Wang
3. Wenwu Zhu
4. Kai Shu
5. Huan Liu
6. Jian Pei
7. Shixia Liu
8. Jiliang Tang

Citations in the corpus

• Social big data: Recent achievements and new challenges (2015, background): “According to the analytics, these diffusion models can be categorized into explanatory and predictive approaches [77].”
• A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (2015, background): “Information diffusion [8, 12] also relies on user activity to describe influence, but focuses on understanding the widespread flow of information.”
• A Survey on Network Embedding (2017, background): “Information diffusion [62] is an ubiquitous phenomenon on the web, especially in social networks.”
• Fake News Detection on Social Media: A Data Mining Perspective (2017, background): “Tracking the life cycle of fake news on social media requires recording essential trajectories of fake news diffusion in general [71], as well as further investigations of the process for specific fake news pieces, such as graph-based models and evolution-based models [27].”
• Searching for superspreaders of information in real-world social media (2014, background): “many shortest paths cross through a node 26 .”
• EvoRiver: Visual Analysis of Topic Coopetition on Social Media (2014, background): “Many information diffusion models assume that multiple memes flow in isolation on social media [18].”
• Influence Maximization on Social Graphs: A Survey (2018, background): “This diffusion phenomenon has been shown to be powerful in many applications [1], such as adoption of political standpoints and technical innovations.”
• Modern temporal network theory: a colloquium (2015, neutral): “[329] and other papers) and is thus not a dynamic system on the network, but there could be more practical motivations for synchronization studies in this direction.”