Abstract
Social networks are the natural space for the spreading of information and influence and have become a media themselves. Several models capturing that diffusion process have been proposed, most of them based on the Independent Cascade (IC) model or on the Linear Threshold (LT) model. The IC model is probabilistic while the LT model relies on the knowledge of an actor to be convinced, reflected in an associated individual threshold. Although the LT-based models contemplate an individual threshold for each actor in the network, the existing studies so far have always considered a threshold of 0.5 equal in all actors (i.e., a simple majority activation criterion).
Our main objective in this work is to start the study on how the dissemination of information on networks behaves when we consider other options for setting those thresholds and how many network actors end up being influenced by this dissemination. For doing so, we consider a recently introduced centrality measure based on the LT model, the Forward Linear Threshold Rank (FwLTR), which is the natural interpretation of the Linear Threshold Rank on directed networks.
We experimentally analyze the ranking properties for several networks in which the influence resistance threshold follows different schemes. Here we consider three different schemes: (1) uniform, in which all players have the same value; (2) random, where each player is assigned a threshold u.a.r. in a prescribed interval; and (3) determined by the value of another centrality measure on the actor. Our results show that the selection has a clear impact on the ranking, even quite significant and abrupt in some cases. We conclude that the social networks ranks that provide the best assignments for the individual thresholds are FwLTR and the well-known PageRank.
Supported by MCIN/AEI/10.13039/501100011033 under grant PID2020-112581GB-C21 (MOTION).
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Notes
- 1.
Infrastructure available at the Research& Development Lab (\(\backslash \)RDlab) of the Universitat Politècnica de Catalunya - BarcelonaTech.
- 2.
Results for the remaining ones can be found in the Appendix.
- 3.
How the thresholds are assigned according to the different centrality measures in each network can be seen in the Appendix.
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Blesa, M.J., Dominguez-Besserer, A., Serna, M. (2025). Thresholds as Mechanisms for Weighting Influence in the Linear Threshold Rank. In: Aiello, L.M., Chakraborty, T., Gaito, S. (eds) Social Networks Analysis and Mining. ASONAM 2024. Lecture Notes in Computer Science, vol 15211. Springer, Cham. https://doi.org/10.1007/978-3-031-78541-2_13
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