The purpose of the influence maximization problem is to determine a subset to maximize the number of affected users. This problem is very crucial for ...
The IRWIM algorithm improves the traditional random walk and adopts multi-task neural network architecture to predict the propagation ability of nodes more ...
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Based on this idea, the paper proposes Representation Learning for Influence Maximization (RLIM) algorithm. The premise of this algorithm is to construct the ...
... improved algorithm that uses the second-order random walks method to generate the influence cascade. The proposed method of generating influence cascade is ...
An improved influence maximization method for social networks based on genetic algorithm. Phys A Stat Mech Appl, 586 (2022), Article 126480. Google Scholar.
Mar 5, 2023 · First, the random walk with restart is improved by an attention mechanism that assigns weights to edges by measuring the similarity between ...
Mar 25, 2022 · by random walk-based node representation learning algo- rithms. The key idea of our method is to bias random walks to cross group boundaries ...
Aug 30, 2022 · The proposed approach is exhaustively evaluated by means of node visualization and classification on multiple benchmark datasets, and achieves ...
Jul 20, 2023 · Walk, a random‐walk approach to graph representation learning. ... influence the chance of crossing a group boundary in the random walk,.
In this paper, we propose a novel method of influence maximization by using the idea of graph embedding and graph neural networks. This study intends to convert ...