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Article analysis:

The article Estimating User Influence Ranking in Independent Cascade Model by ScienceDirect provides a comprehensive overview of the current research on user influence ranking in social networks, and proposes a new approach for estimating user influence rankings using a duplicate forwarding model. The article is well-written and provides an extensive review of the existing literature on this topic, as well as a detailed description of the proposed approach and its advantages over existing methods.

However, there are some potential biases that should be noted when evaluating the trustworthiness and reliability of this article. First, the article does not present both sides equally; it focuses mainly on the advantages of the proposed approach without exploring any potential drawbacks or counterarguments. Additionally, there is no mention of possible risks associated with using this approach, such as privacy concerns or data security issues. Furthermore, while the article does provide evidence for its claims, it does not explore any other sources or evidence that could support or refute its conclusions. Finally, there is some promotional content in the article which could be seen as biased towards promoting the proposed approach over other existing methods.

In conclusion, while this article provides an informative overview of current research on user influence ranking in social networks and presents a new approach for estimating user influence rankings using a duplicate forwarding model, there are some potential biases that should be taken into consideration when evaluating its trustworthiness and reliability.