Tech Innovations & Dataset Analysis to Combat Fake Accounts in Digital Communities

Fake accounts are becoming more and more common in the digital age, which puts a serious strain on online groups' credibility and integrity. Graph Convolutional Networks (GCNs) and Graph Autoencoders are two cutting-edge technologies that have emerged as viable means of detecting and mitigating bogus accounts to address this problem. These cutting-edge machine-learning methods may identify complex patterns and correlations suggestive of fraudulent activity by examining massive datasets that represent online communities as graphs. To differentiate between real and fraudulent accounts, GCNs facilitate efficient information transmission throughout the graph by collecting local and global attributes. A latent space representation of the network structure is created by network Autoencoders, which in the meantime enable effective feature extraction and anomaly detection and promote unsupervised learning. Utilizing GCNs' synergy is the goal of this investigation. The goal of this research is to create strong models that can precisely detect and remove phony accounts from online communities by utilizing the synergy between GCNs and Graph Autoencoders. This research helps to improve the security and dependability of online platforms utilizing thorough dataset analysis and experimentation, which in turn promotes a more trustworthy and healthier digital environment.