Comparing Graph Similarity Measures for Graphical Recognition

Jouili, Salim; Tabbone, Salvatore; Valveny, Ernest

doi:10.1007/978-3-642-13728-0_4

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…In this paper, 1,000,000 nodes were selected from the data, with almost 10,000 of them being fake, and were then mapped into a graph. By adjacency matrix the graph was converted into an matrix to analyze the relations between each node [21]. In order to find relations between all nodes and better separation in fake classes, similarity measures were calculated.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Detecting fake accounts through Generative adversarial network in online social media

Bordbar,

Mohammadrezaei,

Ardalan

et al. 2023

Preprint

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Online social media is integral to human life, facilitating messaging, information sharing, and confidential communication while preserving privacy. Platforms like Twitter, Instagram, and Facebook exemplify this phenomenon. However, users face challenges due to network anomalies, often stemming from malicious activities such as identity theft for financial gain or harm. This paper proposes a novel method using user similarity measures and the Generative Adversarial Network (GAN) algorithm to identify fake user accounts in the Twitter dataset. Despite the problem's complexity, the method achieves an AUC rate of 80% in classifying and detecting fake accounts. Notably, the study builds on previous research, highlighting advancements and insights into the evolving landscape of anomaly detection in online social networks.

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Section: Proposed Methodsmentioning

confidence: 99%

“…To analyze the Twitter dataset, the data was first converted into a graph using similarity measures [21]. At this stage, each user was represented as a node, and each relation between users was represented as an edge.…”

Section: Mapping Social Network's Data To Graphmentioning

confidence: 99%

Detecting fake accounts through Generative adversarial network in online social media

Bordbar,

Mohammadrezaei,

Ardalan

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…To analyze the Twitter Dataset, the data was converted into a graph by utilizing similarity measures [12]. During this process, each user was represented as a node, and each relation between users was represented as an edge.…”

Section: Mapping Social Network's Data Into Graphmentioning

confidence: 99%

Fake Detection in Imbalance Dataset by Semi-Supervised Learning with GAN

Jinus

Saman

Mohammadrezaei

et al. 2023

Preprint

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As social media grows faster, harassment becomes more prevalent, which leads to the consideration of fake detection as a fascinating field among researchers. The graph nature of data, with a large number of nodes, causes different obstacles, including a considerable amount of unrelated features in matrices, as well as high dispersion and imbalanced classes in the dataset. To address these issues, Auto-encoders and a combination of semi-supervised learning and the GAN algorithm, called SGAN, were used. This paper deploys a smaller number of labels and applies SGAN as a classifier. The results of this test showed that the accuracy reached 81% in detecting fake accounts using only 100 labeled samples.

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