Prediction of Information Diffusion Probabilities for Independent Cascade Model

By Kazumi Saito, Ryohei Nakano, Masahiro Kimura. International Conference on Knowledge-Based Intelligent Information & Engineering Systems, 2008

495 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/bce192e0855b8efdf0488f55ef2c31e621b6a747

How researchers see this paper

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

In Artificial Intelligence

1. Yuxiao Dong
2. Jie Tang
3. Kuansan Wang
4. Jiezhong Qiu
5. Kian-Lee Tan
6. Ju Fan
7. Yuchen Li
8. Yanhao Wang

In Statistical and Nonlinear Physics

1. Jie Tang
2. Yuxiao Dong
3. Kuansan Wang
4. Kian-Lee Tan
5. Ju Fan
6. Yuchen Li
7. Yanhao Wang
8. Nhathai Phan

Citations in the corpus

• DeepInf: Social Influence Prediction with Deep Learning (2018, background): “Examples of such work include pairwise influence [19, 39], topic-level influence [42], group formation [2, 38] and structural diversity [14, 29, 46].”
• Social restricted Boltzmann Machine: Human behavior prediction in health social networks (2015, background): “Thus, conventional social influence functions (e.g., information propagation cascade models [8], [17]) may not fit our context.”
• SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model (2011, unknown): “abilities from the available data is studied in [14] and [15].”
• Dynamics of information diffusion and its applications on complex networks (2016, background): “[202] addressed the problem of how to predict the probabilities for the independent cascade model from a set of past propagations.”
• Influence Maximization on Social Graphs: A Survey (2018, background): “Saito et al. [22] are the first to formalize the problem of learning edge probabilities from past propagation actions as a likelihood maximization problem.”
• A Data-Based Approach to Social Influence Maximization (2011, methodology): “[14] study how to learn the probabilities for the IC model from a set of past propagations.”
• Information and Influence Propagation in Social Networks (2013, methodology): “[2004] was an earlier work on this problem, but we prefer to discuss the more rigorous approach of Saito et al. [2008] here.”
• Topic-aware social influence propagation models (2012, unknown): “5 Predictive accuracy In the following, we compare IC, TIC and AIR : The parameters of the model are learned using the EM method in [25], the method in Sect.”
• Analysis of Physical Activity Propagation in a Health Social Network (2014, background): “Another line of study has focused on the problem of learning the influence probabilities on every edge of a social network given an observed log of propagations over this network [4, 21, 24, 28].”