Entropy-Based Social Influence Evaluation in Mobile Social Networks

Peng, Sancheng; Li, Jian; Yang, Aimin

doi:10.1007/978-3-319-27119-4_44

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Additionally, Takaguchi et al [56] proposed using the information entropy method to predict a sequence of conversations among individuals [56]. Peng and others utilized Shannon entropy to demonstrate the focus of Twitter users on different topics compared with the entire system [57]. Kulisiewicz et al [58] suggested that entropy (calculating first-order, second-order, and third-order entropy) could be used to describe the dynamics of human communication mechanisms in social networks, which can help us observe and understand sociological processes in dynamic communities [58].…”

Section: Social Entropymentioning

confidence: 99%

Introducing Entropy into Organizational Psychology: An Entropy-Based Proactive Control Model

Jia,

Wang

2024

Behavioral Sciences

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This paper provides a systematic review of the transfer and quantification of the concept of entropy in multidisciplinary fields and delves into its future applications and research directions in organizational management psychology based on its core characteristics. We first comprehensively reviewed the conceptual evolution of entropy in disciplines such as physics, information theory, and psychology, revealing its complexity and diversity as an interdisciplinary concept. Subsequently, we analyzed the quantification methods of entropy in a multidisciplinary context and pointed out that their calculation methods have both specificity and commonality across different disciplines. Subsequently, the paper reviewed the research on how individuals cope with uncertainty in entropy increase, redefined psychological entropy from the perspective of organizational management psychology, and proposed an “entropy-based proactive control model” at the individual level. This model is built around the core connotation of entropy, covering four dimensions: learning orientation, goal orientation, change orientation, and risk taking. We believe that psychological entropy, as a meta structure of individuals, can simulate, explain, and predict the process of how individuals manage and control “entropy” in an organizational environment from a dynamic perspective. This understanding enables psychological entropy to integrate a series of positive psychological constructs (e.g., lean spirit), providing extensive predictive and explanatory power for various behaviors of individuals in organizations. This paper provides a new direction for the application of the concept of entropy in psychology, especially for theoretical development and practical application in the field of organizational management.

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Section: Social Entropymentioning

confidence: 99%

Introducing Entropy into Organizational Psychology: An Entropy-Based Proactive Control Model

Jia,

Wang

2024

Behavioral Sciences

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“…Another way to measure influence is based on entropy and information theory. Peng et al [ 32 ] use node entropy based upon the degree of a user and interaction frequency entropy to evaluate social influence in mobile social networks. Sun and Ng [ 33 ] use graph entropy based upon the centrality of users to measure the influence of connectors on social networks.…”

Section: Background and Related Workmentioning

confidence: 99%

Influence Cascades: Entropy-Based Characterization of Behavioral Influence Patterns in Social Media

Senevirathna

Gunaratne

Rand

et al. 2021

Entropy

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Influence cascades are typically analyzed using a single metric approach, i.e., all influence is measured using one number. However, social influence is not monolithic; different users exercise different influences in different ways, and influence is correlated with the user and content-specific attributes. One such attribute could be whether the action is an initiation of a new post, a contribution to a post, or a sharing of an existing post. In this paper, we present a novel method for tracking these influence relationships over time, which we call influence cascades, and present a visualization technique to better understand these cascades. We investigate these influence patterns within and across online social media platforms using empirical data and comparing to a scale-free network as a null model. Our results show that characteristics of influence cascades and patterns of influence are, in fact, affected by the platform and the community of the users.

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“…In addition, entropy is an effective tool to describe the complexity and uncertainty of the social impact, and so it has been widely used in social networks. Peng [ 22 ] proposed two concepts, the friend entropy and the interaction frequency entropy, in order to measure the social impact. Sathanur and Jandhyala et al [ 23 ] introduced the transfer entropy to measure the impact of directed causality.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method to Rank Influential Nodes in Complex Networks Based on Tsallis Entropy

Xue-gong

Zhou

Liao

et al. 2020

Entropy

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With the rapid development of social networks, it has become extremely important to evaluate the propagation capabilities of the nodes in a network. Related research has wide applications, such as in network monitoring and rumor control. However, the current research on the propagation ability of network nodes is mostly based on the analysis of the degree of nodes. The method is simple, but the effectiveness needs to be improved. Based on this problem, this paper proposes a method that is based on Tsallis entropy to detect the propagation ability of network nodes. This method comprehensively considers the relationship between a node’s Tsallis entropy and its neighbors, employs the Tsallis entropy method to construct the TsallisRank algorithm, and uses the SIR (Susceptible, Infectious, Recovered) model for verifying the correctness of the algorithm. The experimental results show that, in a real network, this method can effectively and accurately evaluate the propagation ability of network nodes.

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Entropy-Based Social Influence Evaluation in Mobile Social Networks

Cited by 6 publications

References 22 publications

Introducing Entropy into Organizational Psychology: An Entropy-Based Proactive Control Model

Introducing Entropy into Organizational Psychology: An Entropy-Based Proactive Control Model

Influence Cascades: Entropy-Based Characterization of Behavioral Influence Patterns in Social Media

A Novel Method to Rank Influential Nodes in Complex Networks Based on Tsallis Entropy

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