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This tutorial explores emails sent between employees of the Enron Corporation, using an Example Graph on Conode:
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The what
We’ll be exploring the large 500k Enron email Corpus to showcase how Conode can be used for forensic accounting purposes.
The data
The dataset contains emails by employees of Enron Corporation, an American energy, commodities and services company in Texas. These were obtained by the Federal Energy Regulatory Commission during its investigation of widespread accounting fraud within Enron.
For this first session, we’re only focusing on the emails of 26 C-Level executives instead of the full 500k. We have features From , To, CC , BCC which are all email addresses, Subject of Email and Date Email Sent , which all connect to the emails.
The how
We’ll begin by asking our graph some basic questions to understand what is in the dataset and generate distribution plots to visualise the results. We’ll also use the opportunity to interact with the graph and edit the views to better understand what we’re seeing. Next we’ll enrich our graph using the extract agent, and conclude by exploring the graph structure of email connections.
The why
This investigation ultimately aims to identify any evidence of fraud within the emails. The value of Conode lies in its ability to help speed up the process of investigation and function to cross-highlight between views. This allows analysts to have a unified view of the data while querying the graph.