In cybersecurity, graph analysis has become a key methodology for identifying malicious websites and attack campaigns. This approach leverages graph structure and relationships between nodes to identify anomalous patterns or suspicious communities that could be indicative of malicious activity. In this thesis work, a cybersecurity methodology was developed to automatically identify suspicious websites not yet known as malicious, starting from others already known as malicious. To achieve this objective, it was decided to work with different community detection algorithms on some multi-graphs created in Neo4j, a famous graph database management system. These multi-graphs were filled with some preprocessed features, extracted from datasets containing only passive detection information of websites, i.e.