A Novel Graph Centrality Based Approach to Analyze Anomalous Nodes with Negative Behavior

AbstractDetection of different kinds of anomalous behaviors originating from negative ties among actors in online social networks is an unexplored area requiring extensive research. Due to increase in social crimes such as masquerading, bullying, etc., identification and analysis of these activities has become need of the hour. Approaches from two separate, yet, similar research areas, i.e. anomaly detection and negative tie analysis, can be clubbed together to identify negative anomalous nodes. Use of best measures from centrality based (negative ties) and structure based approaches (anomaly detection) can help us identify and analyze the negative ties more efficiently. A comparative analysis has been performed to detect the negative behaviors in online networks using different centrality measures and their relationship in curve fitting anomaly detection techniques. From results it is observed that curve fitting analysis of centrality measures relationship performs better than independent analysis of centrality measures for detecting negative anomalous nodes

Fraud Pattern Detection for NFT Markets

Non-Fungible Tokens (NFTs) enable ownership and transfer of digital assets using blockchain technology. As a relatively new financial asset class, NFTs lack robust oversight and regulations. These conditions create an environment that is susceptible to fraudulent activity and market manipulation schemes. This study examines the buyer-seller network transactional data from some of the most popular NFT marketplaces (e.g., AtomicHub, OpenSea) to identify and predict fraudulent activity. To accomplish this goal multiple features such as price, volume, and network metrics were extracted from NFT transactional data. These were fed into a Multiple-Scale Convolutional Neural Network that predicts suspected fraudulent activity based on pattern recognition. This approach provides a more generic form of time series classification at different frequencies and timescales to recognize fraudulent NFT patterns. Results showed that over 80% of confirmed fraudulent cases were identified by modeling (recall). For every predicted fraud case, the model was correct 50% of the time (precision). Investors, regulators, and other entities can use these techniques to reduce risk exposure to NFT fraudulent activity

Analyzing the effectiveness of graph metrics for anomaly detection in online social networks

Online social networks can be modelled as graphs; in this paper, we analyze the use of graph metrics for identifying users with anomalous relationships to other users. A framework is proposed for analyzing the effectiveness of various graph theoretic properties such as the number of neighbouring nodes and edges, betweenness centrality, and community cohesiveness in detecting anomalous users. Experimental results on real-world data collected from online social networks show that the majority of users typically have friends who are friends themselves, whereas anomalous users’ graphs typically do not follow this common rule. Empirical analysis also shows that the relationship between average betweenness centrality and edges identifies anomalies more accurately than other approaches