Analyzing Community-Aware Centrality Measures Using the Linear Threshold Model

Rajeh, Stephany; Yassin, Ali; Jaber, Ali; Cherifi, Hocine

doi:10.1007/978-3-030-93409-5_29

Cited by 3 publications

(3 citation statements)

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“…The linear threshold model simulates the spread and adoption of ideas and behaviours through a network and has previously been applied to evaluate the performance of community-aware centrality scores (Rajeh et al 2022). In the linear threshold model, nodes can be in either of two states, that is, they can be active or inactive.…”

Section: Evaluation With the Linear Threshold Modelmentioning

confidence: 99%

“…To evaluate the performance of map equation centrality, we apply it to twelve empirical networks to identify influential nodes. Like in previous work on centrality scores, we contrast our predictions with the spreading power of nodes obtained from simulations of a Susceptible-Infected-Recovered (SIR) disease-spreading model Rajeh et al 2021) and the adoptions of ideas modelled by the linear threshold model (Rajeh et al 2022). For comparison, we include degree centrality as a local measure, betweenness centrality as a global measure, as well as three other community-aware centrality measures in our evaluation.…”

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confidence: 99%

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Map equation centrality: community-aware centrality based on the map equation

2022

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To measure node importance, network scientists employ centrality scores that typically take a microscopic or macroscopic perspective, relying on node features or global network structure. However, traditional centrality measures such as degree centrality, betweenness centrality, or PageRank neglect the community structure found in real-world networks. To study node importance based on network flows from a mesoscopic perspective, we analytically derive a community-aware information-theoretic centrality score based on network flow and the coding principles behind the map equation: map equation centrality. Map equation centrality measures how much further we can compress the network’s modular description by not coding for random walker transitions to the respective node, using an adapted coding scheme and determining node importance from a network flow-based point of view. The information-theoretic centrality measure can be determined from a node’s local network context alone because changes to the coding scheme only affect other nodes in the same module. Map equation centrality is agnostic to the chosen network flow model and allows researchers to select the model that best reflects the dynamics of the process under study. Applied to synthetic networks, we highlight how our approach enables a more fine-grained differentiation between nodes than node-local or network-global measures. Predicting influential nodes for two different dynamical processes on real-world networks with traditional and other community-aware centrality measures, we find that activating nodes based on map equation centrality scores tends to create the largest cascades in a linear threshold model.

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Section: Evaluation With the Linear Threshold Modelmentioning

confidence: 99%

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confidence: 99%

Map equation centrality: community-aware centrality based on the map equation

2022

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“…The influence model refers to the influence of an individual's activation behavior affected by the states or behaviors of its neighbors [54], including the linear threshold models [88] and the independent cascade models, explaining the dissemination from the perspectives of probability and threshold, respectively, applying widely in various fields, such as influence node detection [89,90], link prediction [11], and behavioral propagation [91].…”

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confidence: 99%

A Survey of Information Dissemination Model, Datasets, and Insight

Liu,

Zhang,

Shi

et al. 2023

Mathematics

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Information dissemination refers to how information spreads among users on social networks. With the widespread application of mobile communication and internet technologies, people increasingly rely on information on the internet, and the mode of information dissemination is constantly changing. Researchers have performed various studies from mathematical modeling and cascade prediction perspectives to explore the previous problem. However, lacking a comprehensive review of the latest information dissemination models hinders scientific development. As a result, it is essential to review the latest models or methods. In this paper, we review information dissemination models from the past three years and conduct a detailed analysis, such as explanatory and predictive models. Moreover, we provide public datasets, evaluation metrics, and interface tools for researchers focusing more on algorithm design and modeling. Finally, we discuss the model application and future research directions. This paper aims to understand better the research progress and development trends for beginners and guide future research endeavors. We believe this article will attract more researchers’ interest and attention to the information dissemination field on social networks.

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