The Effectiveness of Edge Centrality Measures for Anomaly Detection

Mitchell, Candice; Agrawal, Rajeev; Parker, Joshua

doi:10.1109/bigdata47090.2019.9006468

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Detection of the central or influential actors in a network is mainly out on social media, such as Twitter [7]- [12]. Besides, influential actors implement other fields, such as, cybercrime [13]- [15] and e-commerce [16]. Over the last decade, numerous methods have been developed to detect the influential actors of hate speech in social networks, one of which is the Collective Influence (CI) method.…”

Section: Introductionmentioning

confidence: 99%

An extended approach of weight collective influence graph for detection influence actor

Martono

Azhari

Mustofa

2022

Int. J. Adv. Intell. Informatics

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Over the last decade, numerous methods have been developed to detect the influential actors of hate speech in social networks, one of which is the Collective Influence (CI) method. However, this method is associated with unweighted datasets, which makes it inappropriate for social media, significantly using weight datasets. This study proposes a new CI method called the Weighted Collective Influence Graph (WCIG), which uses the weights and neighbor values to detect the influence of hate speech. A total of 49, 992 Indonesian tweets were and extracted from Indonesian Twitter accounts, from January 01 to January 22, 2021. The data collected are also used to compare the results of the proposed WCIG method to determine the influential actors in the dissemination of information. The experiment was carried out two times using parameters ∂=2 and ∂=4. The results showed that the usernames bernacleboy and zack_rockstar are influential actors in the dataset. Furthermore, the time needed to process WCIG calculations on HPC is 34-75 hours because the larger the parameter used, the greater the processing time.

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