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Efficient Influence Maximization in Social-Networks Under Independent Cascade Model - ScienceDirect
Source: webvpn.hainanu.edu.cn
Article summary:
1. This paper proposes an efficient degree heuristic algorithm under the Independent Cascade Model (ICM) to identify top k influential users in a social network.
2. A modification of ICM is proposed which derives propagation probability based on similarity metrics.
3. The proposed work is evaluated on two network datasets and results show that the degree heuristic algorithm has influence spread far better than many centrality based heuristics and close to benchmark greedy algorithm.
Article analysis:
The article “Efficient Influence Maximization in Social-Networks Under Independent Cascade Model” provides a detailed overview of the challenges associated with analyzing social networks as an information diffusion/marketing platform, and presents a proposed solution for identifying top k influential users in such networks. The article is well-written and provides clear explanations of the concepts discussed, as well as detailed descriptions of the algorithms used to address the challenge of influence maximization.
The article does not appear to be biased or one-sided, as it presents both sides of the argument fairly and objectively. It also provides evidence for its claims, citing relevant research studies and other sources throughout the text. Furthermore, it does not appear to contain any promotional content or partiality towards any particular viewpoint or opinion.
However, there are some points that could have been explored further in order to provide a more comprehensive analysis of influence maximization in social networks. For example, while the article discusses various algorithms used for influence maximization, it does not discuss potential risks associated with using these algorithms or how they can be mitigated. Additionally, while it mentions various metrics used for deriving propagation probabilities, it does not provide any details on how these metrics are calculated or what factors they take into account when determining propagation probabilities.
In conclusion, this article provides a thorough overview of influence maximization in social networks and presents an effective solution for identifying top k influential users within such networks. While there are some points that could have been explored further in order to provide a more comprehensive analysis of this topic, overall this article appears to be reliable and trustworthy source of information on this subject matter.
Topics for further research:
Risk assessment for influence maximization algorithms
Propagation probability calculation methods
Mitigation strategies for influence maximization algorithms
Influence maximization in social networks: best practices
Influence maximization in social networks: ethical considerations
Influence maximization in social networks: case studies