Network Centrality Measures: Role and Importance in Social Networks

Saxena, Rahul; Jadeja, Mahipal

doi:10.1007/978-981-16-3398-0_2

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…Nodes with high eigenvector centrality exhibit strong connections not only with other nodes but specifically with nodes that possess high centrality themselves. This metric is useful for identifying important nodes that have a wide and influential reach within a network [27]. It is particularly valuable for understanding the pattern of information flow or influence in social networks.…”

Section: Centrality Measuresmentioning

confidence: 99%

“…Harmonic centrality is a network metric that measures the significance of a node in a network by calculating the sum of the inverse values of its shortest path lengths to all other nodes. Nodes with high harmonic centrality are characterized by their ability to be rapidly accessed from other nodes, enabling effective communication within the larger network [27].…”

Section: Centrality Measuresmentioning

confidence: 99%

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A Centrality Metrics Approach to Analyzing Temporal Spread Patterns of Misinformation in Social Networks

Duzen,

Riveni,

Aktas

2024

Preprint

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This study examines misinformation in social networks with well-known network science metrics. We utilize an already available dataset to analyze tweets related to COVID-19 vaccinations. The methodology we use in this study includes the creation of separate networks for retweets, replies, and mentions. We utilized various centrality metrics to assess the importance of individual tweets such as centrality metrics that primarily consider the network’s topology and connectivity, taking into account factors such as the number of connections, the importance of connections , information flow, and indirect influences. One of our objectives is to determine the length of time that tweets linked to unreliable sources exist within a network through reactions. The empirical results of our study reveal a noteworthy association: tweets that have longer durations tend to be posted by influential accounts within the network, in contrast to tweets that have shorter durations and demonstrate reduced influence. Our study contributes to the advancement of our understanding of the dynamics of misinformation spread, by our results regarding misinforma-tion persistence within social networks. Our observations show potential for informing approaches designed to address misinformation and disin-formation, especially in the setting of public health emergencies like as the current COVID-19 outbreak.

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Section: Centrality Measuresmentioning

confidence: 99%

Section: Centrality Measuresmentioning

confidence: 99%