Abstract
The study of information spread in social networks has applications in viral marketing, rumour modelling, and opinion dynamics. Often, it is crucial to identify a small set of influential agents that maximize the spread of information (cases which we refer to as being budget-constrained). These nodes are believed to have special topological properties and reside in the core of a network. We introduce the concept of nucleus decomposition, a clique based extension of core decomposition of graphs, as a new method to locate influential nodes. Our analysis shows that influential nodes lie in the k-nucleus subgraphs and that these nodes outperform lower-order decomposition techniques such as truss and core, while simultaneously focusing on a smaller set of seed nodes. Examining different diffusion models on real-world networks, we provide insights as well into the value of the degree centrality heuristic.
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Notes
- 1.
[19] showed that (3, 4)-nucleus provides high-quality outputs in terms of density and network hierarchy; e.g., it finds both small sets of high density and large sets of low density.
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Agarwal, R.R., Cohen, R., Golab, L., Tsang, A. (2020). Locating Influential Agents in Social Networks: Budget-Constrained Seed Set Selection. In: Goutte, C., Zhu, X. (eds) Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science(), vol 12109. Springer, Cham. https://doi.org/10.1007/978-3-030-47358-7_2
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