Generic anomalous vertices detection utilizing a link prediction algorithm

Kagan, Dima; Elovici, Yuval; Fire, Michael

doi:10.1007/s13278-018-0503-4

Cited by 30 publications

(19 citation statements)

References 31 publications

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“…The results show that both types of measures are almost equivalently effective. Kagan et al (2018) have created a generic unsupervised learning algorithm to detect anomalous vertices. The algorithm creates a link predication classifier based on the graph's topology in its first iteration to predict the possibility of existence of an edge with high accuracy.…”

Section: Detecting Fake Users On Social Media With a Graph Databasementioning

confidence: 99%

“…The second dataset is a friendship network of a school class from 1880/81 (Kagan, 2017). It is likely the earliest known social media dataset and it is used in the study by Kagan et al (2018). We call it the "class" dataset.…”

Section: Figurementioning

confidence: 99%

“…Figure 2b is another arrangement of the graph, which shows us the fake nodes coloured purple. The third dataset contains data about Twitter users obtained using an application programming interface (API) crawler (Kagan, 2017), and it is also used in the study by Kagan et al (2018). The crawler capped the maximum number of friends and followers to 1,000 for each user because the number of friends and followers is unlimited.…”

Section: Figurementioning

confidence: 99%

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Detecting Fake Users on Social Media with a Graph Database

Zhao

Weber

2021

TAR

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Social media has become a major part of people’s daily lives as it provides users with the convenience to connect with people, interact with friends, share personal content with others, and gather information. However, it also creates opportunities for fake users. Fake users on social media may be perceived as popular and influential if not detected. They might spread false information or fake news by making it look real, manipulating real users into making certain decisions. In computer science, a social network can be treated as a graph, which is a data structure consisting of nodes being the social media users, and edges being the connections between users. Graph data can be stored in a graph database for efficient data analysis. In this paper, we propose using a graph database to achieve an increased scalability to accommodate larger graphs. Centrality measures as features were extracted for the random forest classifier to successfully detect fake users with high precision, recall, and accuracy. We have achieved promising results especially when compared with previous studies.

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Section: Detecting Fake Users On Social Media With a Graph Databasementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

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Detecting Fake Users on Social Media with a Graph Database

Zhao

Weber

2021

TAR

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“…Numerous studies have explored machine learning algorithms, graph analysis and classification algorithms for identifying the fake users. Recently, a double layered meta-classifier employing topology based features was proposed by Kagan et al [61] to detect fake profiles. Similar to link formation, investigating the mechanism of termination of links referred to as link dissolution is paramount.…”

Section: Challenges In Link Prediction Techniquesmentioning

confidence: 99%

A review of similarity measures and link prediction models in social networks

al.¹

2020

IJCDS

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Social network is a web-based platform which enables people to share information, make new connections and explore various events that occur in society. In social networks, link prediction techniques are widely used to discover new indirect relationships that may occur in the future. These techniques are also utilized to effectively detect missing links in any monitored network. This study presents a concise review of the similarity measures, techniques employed in predicting future links and application of link prediction with emphasis on dynamic networks. An analysis of available models for link prediction and their suitability for heterogeneous, large, static or dynamic networks is also presented.

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“…Distinct methods have been used for finding the fake accounts based on their feature similarities, comparability of friend networks, profile analyses for a time interval along with their IP address. [8] Provides an unsupervised 2-layer Meta classification technique that could identify the uncontrollable nodes in a difficult network by utilizing the extraction features of the graph topology. It is also verified that the presented technique is utilized for detecting both the fake and real clients in the network.…”

Section: Introductionmentioning

confidence: 99%

Social Networks Fake Account and Fake News Identification with Reliable Deep Learning

Kanagavalli¹,

Priya²

2022

Intelligent Automation &Amp; Soft Computing

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Recent developments of the World Wide Web (WWW) and social networking (Twitter, Instagram, etc.) paves way for data sharing which has never been observed in the human history before. A major security issue in this network is the creation of fake accounts. In addition, the automatic classification of the text article as true or fake is also a crucial process. The ineffectiveness of humans in distinguishing the true and false information exposes the fake news as a risk to credibility, democracy, logical truth, and journalism in government sectors. Besides, the automatic fake news or rumors from the social networking sites is a major research area in the field of social media analytics. With this motivation, this paper develops a new reliable deep learning (DL) based fake account and fake news detection (RDL-FAFND) model for the social networking sites. The goal of the RDL-FAFND model is to resolve the major problems involved in the social media platforms namely fake accounts, fake news/rumor identification. The presented RDL-FAFND model detects the fake account by the use of a parameter tuned deep stacked Auto encoder (DSAE) using the krill herd (KH) optimization algorithm for detecting the fake social networking accounts. Besides, the presented RDL-FAFND model involves an ensemble of the machine learning (ML) models with different linguistic features (EML-LF) for categorizing the text as true or fake. An extensive set of experiments have been carried out for highlighting the superior performance of the RDL-FAFND model. A detailed comparative results analysis has stated that the presented RDL-FAFND model is considerably better than the existing methods.

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Generic anomalous vertices detection utilizing a link prediction algorithm

Cited by 30 publications

References 31 publications

Detecting Fake Users on Social Media with a Graph Database

Detecting Fake Users on Social Media with a Graph Database

A review of similarity measures and link prediction models in social networks

Social Networks Fake Account and Fake News Identification with Reliable Deep Learning

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