Node anomaly detection in social networks using cohesive non-local graph convolutional network

Users connect with one another and develop relationships on social media platforms. These users have a collection of personal information about themselves on these platforms and communicate with one another. Social networks are becoming more prevalent all across the globe. With all of its advantages, criminality and fraudulent conduct in this medium are on the rise. As a result, there is an urgent need to detect abnormalities in these networks before they do substantial harm. Social network analysis uses graph data structure to represent and manage data. Graphs store data and capture relationships that exist between the nodes. Graphs are a complicated kind of data representation in which each data entry contains attributes and is also connected to other data entries traditional non-deep learning approaches are failing to perform effectively when the size and scope of real-world social networks rise in numbers.