HGIM: Influence maximization in diffusion cascades from the perspective of heterogeneous graph
Author:
Funder
National Natural Science Foundation of China
Sichuan University
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-04711-4.pdf
Reference35 articles.
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2. Bakshy E, Hofman JM, Mason, WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: International Conference on Web Search and Data Mining, pp. 65–74. https://doi.org/10.1145/1935826.1935845
3. Berahmand K, Bouyer A, Samadi N (2018) A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks. Chaos, Solitons & Fractals 110:41–54. https://doi.org/10.1016/j.chaos.2018.03.014
4. Berahmand K, Samadi N, Sheikholeslami SM (2018) Effect of rich-club on diffusion in complex networks. Int J Mod Phys B 32(12):1850142. https://doi.org/10.1142/S0217979218501424
5. Bhattacharya S, Gaurav K, Ghosh S (2019) Viral marketing on social networks: An epidemiological perspective. Phys A 525:478–490. https://doi.org/10.1016/j.physa.2019.03.008
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