Most social networks influence maximization problem are based on the following two basic propagation model: Independent Cascade Model and Linear Threshold Model ...
We use community-discovery algorithm to find the opinion leaders, k members are selected from the opinion leaders so that we get the influence maximization set ...
This paper proposes the first scalable influence maximization algorithm tailored for the linear threshold model, which is scalable to networks with millions ...
Bibliographic details on Scalable Influence Maximization in Social Networks Using the Community Discovery Algorithm.
To this end, we design algorithms that leverage community structure from available training graphs to generate graphs with similar properties. (4) We ...
The study employs the linear threshold and independent cascade models to assess the speed of influence propagation. Results suggest that choosing seed nodes ...
Oct 22, 2024 · Influence maximization problem aims to find a set of nodes with the highest diffusion in social networks in order to maximize diffusion in ...
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What is influence maximization in social networks?
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Influence maximization (IM) is the process of choosing a set of seeds from a social network so that the most individuals will be influenced by them.
We empirically evaluate our new algorithm against various known existing strategies comparing runtime, individual influence spread, groups activated above ...
This paper highlights the potential advantages of combining heuristic computing and role embedding to solve IM problems.