Detecting fake accounts through Generative Adversarial Network in online social media

Nowadays, online social media has become an inseparable part of human life, also this phenomenon is being used by individuals to send messages and share files via videos and images. Twitter, Instagram, and Facebook are well-known samples of these networks. One of the main challenges of privacy for users in these networks is anomalies in security. Anomalies in online social networks can be attributed to illegal behavior, such deviance is done by malicious people like account forgers, online fraudsters, etc. This paper proposed a new method to identify fake user accounts by calculating the similarity measures among users, applying the Generative Adversarial Network (GAN) algorithm over the Twitter dataset. The results of the proposed method showed, accuracy was able to reach 98.1% for classifying and detecting fake user accounts

Comparing Graph Similarity for Graphical Recognition

The original publication is available at www.springerlink.com. 8th International Workshop, GREC 2009, La Rochelle, France, July 22-23, 2009. Selected PapersIn this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique

Improving Product-related Patent Information Access with Automated Technology Ontology Extraction

Ph.DDOCTOR OF PHILOSOPH

Comparing Graph Similarity Measures for Graphical Recognition

In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique