A <scp>Hurst</scp>‐based diffusion model using time series characteristics for influence maximization in social networks

Saxena, Bhawna; Saxena, Vikas; Anand, Nishit; Hassija, Vikas; Chamola, Vinay; Hussain, Amir

doi:10.1111/exsy.13375

Cited by 2 publications

(1 citation statement)

References 58 publications

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“…OSNs, or online social networks [20], have grown significantly in popularity recently because of their applications in a variety of real-world fields, including social awareness campaigns, recommendation systems, and marketing. The model selects a minimal number of seed nodes, and if 𝐻 > 0.5, it only activates the inactive successor of each seed node.…”

Section: A Applications Of Hurst Exponent To Social Computingmentioning

confidence: 99%

Maximum Ismail’s Second Entropy Formalism of Heavy-Tailed Queues with Hurst Exponent Heuristic Mean Queue Length Combined with Potential Applications of Hurst Exponent to Social Computing and Connected Health

A Mageed

2024

Preprint

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The theory of Ismail's non-extensive maximum entropy solution (NME) is described in detail. It is used as an inductive inference technique for heavy-tailed queues with a non-robust mean queue length and a non-extensive "long-range" interaction. In our novel method, we substitute the non-robust mean queue length for the conventional Pollaczeck-Khinchin mean queue length. In other words, the new non-extensivity parameter q will be included in the resulting state probability function. Numerical portraits are provided to capture the influential effect of the derived formalism, , on the stable M//1 queue with heavy tails. More potentially, some applications of Hurst Exponent to social computing and connected health are provided. Conclusion with some challenging open problems and possible future research pathways are given.

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Section: A Applications Of Hurst Exponent To Social Computingmentioning

confidence: 99%

Maximum Ismail’s Second Entropy Formalism of Heavy-Tailed Queues with Hurst Exponent Heuristic Mean Queue Length Combined with Potential Applications of Hurst Exponent to Social Computing and Connected Health

A Mageed

2024

Preprint

View full text Add to dashboard Cite

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Generalized hop‐based approaches for identifying influential nodes in social networks

Biswas,

Abbasi,

Chakrabortty

2024

Expert Systems

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Locating a set of influential users within a social network, known as the Influence Maximization (IM) problem, can have significant implications for boosting the spread of positive information/news and curbing the spread of negative elements such as misinformation and disease. However, the traditional simulation‐based spread computations under conventional diffusion models render existing algorithms inefficient in finding optimal solutions. In recent years, hop and path‐based approaches have gained popularity, particularly under the cascade models to address the scalability issue. Nevertheless, these existing functions vary based on the considered hop‐distance and provide no guidance on capturing spread sizes beyond two‐hops. In this paper, we introduce Hop‐based Expected Influence Maximization (HEIM), an approach utilizing generalized functions to compute influence spread across varying hop‐distances in conventional diffusion models. We extend our investigation to the Linear Threshold (LT) model, in addition to the Independent Cascade (IC) and Weighted Cascade (WC) models, filling a gap in current literature. Our theoretical analysis shows that the proposed functions preserve both monotonicity and submodularity, and the proposed HEIM algorithm can achieve an approximation ratio of under a limited hop‐measures, whereas a multiplicative ‐approximation under global measures. Furthermore, we show that expected spread methods can serve as a better benchmark approach than existing simulation‐based methods. The performance of the HEIM algorithm is evaluated through experiments on three real‐world networks, and is compared to six other existing algorithms. Results demonstrate that the three‐hop based HEIM algorithm achieves superior solution quality, ranking first in statistical tests, and is notably faster than existing benchmark approaches. Conversely, the one‐hop‐based HEIM offers faster computation while still delivering competitive solutions, providing decision‐makers with flexibility based on application needs.

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A Hurst‐based diffusion model using time series characteristics for influence maximization in social networks

Cited by 2 publications

References 58 publications

Maximum Ismail’s Second Entropy Formalism of Heavy-Tailed Queues with Hurst Exponent Heuristic Mean Queue Length Combined with Potential Applications of Hurst Exponent to Social Computing and Connected Health

Maximum Ismail’s Second Entropy Formalism of Heavy-Tailed Queues with Hurst Exponent Heuristic Mean Queue Length Combined with Potential Applications of Hurst Exponent to Social Computing and Connected Health

Generalized hop‐based approaches for identifying influential nodes in social networks

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