This analysis of the Enron Email Dataset focusses on 3 tasks: Anomaly Detection, Social Network Analysis, and Email Body Analysis.

Anomaly detection algorithm for social networks using Graph Neural Networks by leveraging graph parameteres, between centrality, degree, closeness, on Enron and Twitter datasets

Anomaly Detection on the Enron financial-email dataset, using specialised unsupervised machine learning algorithms: One-class SVM, Isolation Forest, LOF.

Repository for Social Media Mining Final Project

Deep Learning Final Project 2025

CS445 Machine Learning Term Project

A time-series anomaly detection model (simple RNN) built using TensorFlow and the Enron Emails dataset.

Implementing a Hopfield Memory Network to "remember" anomalous incidents within a social network (in this case Enron employees)

This project analyze e-mail exchanges of several people in Enron company using Time Series, Graph Analysis & Anomaly Detection

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A Robust Active Learning Framework for Graph Anomaly Detection (GAD). Optimized implementation of GraphPart adapted for class-imbalance scenarios, supporting datasets like Weibo, Reddit, and Enron.

Data mining and network analysis of the Enron email dataset to detect anomalous communication patterns and potential fraudulent behavior. Implements graph-based anomaly detection using Python, NetworkX, and statistical outlier analysis to identify suspicious email activity.

Forensic analysis of the Enron Email Corpus to detect fraud patterns. Features a Python/PostgreSQL ETL pipeline, SQL-based anomaly detection, and network visualization via Tableau. Google Data Analytics Capstone project simulating a financial crime investigation.