Fake news is spreading rapidly on social media, making it essential for social networking sites to detect and prevent its spread. To achieve this, graph analysis and visualization techniques can be employed to understand how fake news spreads online. A comprehensive fake news detection process involves understanding networks and utilizing Neo4j and the KeyLines graph visualization toolkit. The approach combines automated detection using a Neo4j graph database with manual investigation powered by a KeyLines graph visualization tool. This hybrid approach helps identify high-risk content and accounts, detects abnormal behavior, and uncovers new behaviors that may indicate fake news. By leveraging graph databases and visualization tools, social networking sites can efficiently detect and prevent the spread of fake news, protecting users from misinformation.