Anomaly Detection in Social Networks: A Taxonomy of Methods, Research Challenges, and Future Directions

In today's world, social networks have become one of the most important communication platforms and are gradually turning into essential and inseparable parts of daily life. Every day, a huge amount of data is transferred through these networks; therefore, ensuring user security has become a necessity. For this reason, anomaly detection in social networks such as identifying suspicious users and spammers-plays an important role in preventing the spread of fake information, fraud, and scams. These anomalies have different types. In this paper, we conducted a review and analysis of anomaly detection methods and proposed several categorizations for these approaches. Then, based on our proposed categorization, we evaluated and compared the methods, identified the existing challenges, and, as far as possible, suggested solutions to address them.

کلیدواژه ها:

Social Network ، Anomaly Detection ، Anomaly Detection Classification ، Similarity Measure ، Evaluation Function