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Overview

This is a temporal hypergraph dataset, which here means a sequence of timestamped hyperedges where each hyperedge is a set of nodes. In email communication, messages can be sent to multiple recipients. In this dataset, nodes are email addresses at Enron, and a hyperedge is comprised of the sender and all recipients of the email. Only email addresses from a core set of employees are included. Timestamps are in ISO8601 format.

This dataset was collected and prepared by the CALO Project (A Cognitive Assistant that Learns and Organizes). It contains data from about 150 users, mostly senior management of Enron, organized into folders. The corpus contains a total of about 0.5M messages. This data was originally made public and posted to the web by the Federal Energy Regulatory Commission during its investigation.

The email dataset was later purchased by Leslie Kaelbling at MIT and turned out to have a number of integrity problems. A number of folks at SRI, notably Melinda Gervasio, worked hard to correct these problems, and it is thanks to them that the dataset is available. The dataset here does not include attachments, and some messages have been deleted "as part of a redaction effort due to requests from affected employees". Invalid email addresses were converted to something of the form user@enron.com whenever possible (i.e., the recipient is specified in some parseable format like "Doe, John" or "Mary K. Smith") and to no_address@enron.com when no recipient was specified.

Statistics

Some basic statistics of this dataset are:

• number of nodes: 148
• number of timestamped hyperedges: 10,885
• distribution of the connected components:

Component Size, Number

• 143, 1
• 1, 5

Source of original data

Source: email-Enron dataset

References

If you use this dataset, please cite these references:

• Simplicial closure and higher-order link prediction. Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg. Proceedings of the National Academy of Sciences (PNAS), 2018.
• Enron Email Dataset, William Cohen, 2015.

This dataset contains the data from roughly two years of operating PrivacyMail.info, an Open Source Email privacy measurement platform. It contains slightly over 500.000 commercial newsletters, as crowdsourced by users of PrivacyMail.info. You can find the methodology discussed in our paper: Max Maass, Stephan Schwär, and Matthias Hollick. "Towards transparency in email tracking." Annual Privacy Forum, 2019. The source code can be found on github.com/privacymail/privacymail

Please note that, due to its crowdsourced nature, this dataset is a sample of opportunity - it is not representative for all newsletters on the Internet, and likely contains biases based on how it was collected. Notably, German-language newsletters will likely be heavily over-represented.

Dataset Structure
The dataset is structured as follows: On the top level are folders describing the website the newsletter belongs to. Inside that folder are subfolders for each identity that was registered for that website. Inside each of these folders are a series of .eml files that represent the received email messages.

Copyright and Licensing
This dataset is set to non-public due to copyright concerns: The contents of the email messages are (presumably) protected by copyright in most jurisdictions. Most copyright doctrines contain exceptions for non-commercial research use - thus, we feel it is appropriate and acceptable to share the data on a case-by-case basis, the same way we did before shutting down PrivacyMail.info. When requesting access to the data, please briefly describe what research you want to conduct with it, and we will grant you access.

We thus do not put any explicit license on this dataset. Please do not share the raw data publicly. We request that you cite the above-mentioned paper and this dataset in any publications that result from it.

This is a collection of text data from 160 emails. For each email, we have included the subject, text, and type of phishing email. The four types of emails included in the dataset are fraud, false positives (legitimate emails), phishing, and commercial spam. 40 of each type of email are in the dataset. This type of data can be used to help build a more complex email spam blocker and could have applications in cybersecurity.

Andrew Wharton's Actionable US Consumer Email Database hosts over 650 million email addresses that have been active within the last 36 months. This database is fully CAN-SPAM compliant and 100% opted-in for Third Party Use.

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The Andrews Wharton Actionable US Consumer Email Database is updated monthly. A complete replacement database or new adds are available as update files.

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Get the count of unique users that connected to Exchange Online using any email app.

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EU email communication network

Dataset information

The network was generated using email data from a large European research
institution. For a period from October 2003 to May 2005 (18 months) we have
anonymized information about all incoming and outgoing email of the research
institution. For each sent or received email message we know the time, the
sender and the recipient of the email. Overall we have 3,038,531 emails between 287,755 different email addresses. Note that we have a complete email graph for only 1,258 email addresses that come from the research institution.
Furthermore, there are 34,203 email addresses that both sent and received email within the span of our dataset. All other email addresses are either
non-existing, mistyped or spam.

Given a set of email messages, each node corresponds to an email address. We
create a directed edge between nodes i and j, if i sent at least one message to j.

Dataset statistics

Nodes 265214
Edges 420045
Nodes in largest WCC 224832 (0.848)
Edges in largest WCC 395270 (0.941)
Nodes in largest SCC 34203 (0.129)
Edges in largest SCC 151930 (0.362)
Average clustering coefficient 0.3093
Number of triangles 267313
Fraction of closed triangles 0.004106
Diameter (longest shortest path) 13
90-percentile effective diameter 4.5

Source (citation)

J. Leskovec, J. Kleinberg and C. Faloutsos. Graph Evolution: Densification and Shrinking Diameters. ACM Transactions on Knowledge Discovery from Data (ACM
TKDD), 1(1), 2007.

Files
File Description
email-EuAll.txt.gz Email network of a large European Research Institution

Enron email network

Dataset information

Enron email communication network covers all the email communication within a
dataset of around half million emails. This data was originally made public,
and posted to the web, by the Federal Energy Regulatory Commission during its
investigation. Nodes of the network are email addresses and if an address i
sent at least one email to address j, the graph contains a directed edge from i to j. Note that non-Enron email addresses act as sinks and sources in the
network as we only observe their communication with the Enron email addresses.

The Enron email data was originally released by William Cohen at CMU.

Dataset statistics
Nodes 36692
Edges 367662
Nodes in largest WCC 33696 (0.918)
Edges in largest WCC 361622 (0.984)
Nodes in largest...

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Total number of emails sent and received across the FSA PSN network per month.

This dataset offers a high-level look at the web service and the methods it has to offer for public consumption.

To quote the data source: "This dataset was collected and prepared by the CALO Project (A Cognitive Assistant that Learns and Organizes). It contains data from about 150 users, mostly senior management of Enron, organized into folders. The corpus contains a total of about 0.5M messages. This data was originally made public, and posted to the web, by the Federal Energy Regulatory Commission during its investigation. The email dataset was later purchased by Leslie Kaelbling at MIT, and turned out to have a number of integrity problems. A number of folks at SRI, notably Melinda Gervasio, worked hard to correct these problems, and it is thanks to them (not me) that the dataset is available. The dataset here does not include attachments, and some messages have been deleted "as part of a redaction effort due to requests from affected employees". Invalid email addresses were converted to something of the form user@enron.com whenever possible (i.e., recipient is specified in som

Context

The data are obtained from a hypothetical email campaign by serving 3 different Subject Lines across a three month period. There exist three tables for you to play around.

For this campaign, we consider as valid responses only the emails which were opened at the Sent Date (i.e. Sent_Date should be equal to Responded_Date.

Modelling

Build a model which can predict the Open Rate based on the Customer’s attributes and the SubjetLine_ID received!

Dataset

This dataset was created by Aman Gupta

Released under Apache 2.0

Contents

A good DATA is crucial for any business or organization to grow the network. This is because all relevant details about the company and user are stored in the database. Your companies have benefited from using our email database to extract their prospect's details.

It is a well-known fact that LinkedIn gives you the opportunity to expand your business network. You can easily connect with your prospects, directly or through mutual connections, by using search keywords related to their name, company, profile, address, etc. However, we're a leading data provider, with us you do not need to do such a thing. Our Professional's email database contains all the necessary business information from your prospects. There are several ways to access them (especially email addresses and phone numbers).

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Monthly summary of emails sent and received by NICS network

About this Dataset

You will get an active email list for real and active buyers who make regular purchases through Amazon and other e-commerce sites. This email list contains 100% original email address. You can also use these emails to increase visits to your website, blog, or YouTube channel. I offer you now, a great treasure to use whenever you want.

So don't waste your time and start boosting your ecommerce business online.

The buyers will be from:

United States of America Canada Europe Union

$ There are no duplicate emails $ No fake IDs $ Audiences ready to buy

Category

Markets

Keywords

market,emails,email ma,list,buyer

Row Count

70150

Price

$90.00

The dataset consists of a CSV file containing of 300 generated email spam messages. Each row in the file represents a separate email message, its title and text. The dataset aims to facilitate the analysis and detection of spam emails. The dataset can be used for various purposes, such as training machine learning algorithms to classify and filter spam emails, studying spam email patterns, or analyzing text-based features of spam messages.

• Full B2B Contact Data info for 120M US Business Contacts
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• B2B Technographics and Firmographic elements available
• Safe for you to Email - We Email Newsletters to the Data