1 Enron
Note: Very high variance in node degrees. Data can be considered threaded and directed, although http://snap.stanford.edu/data/email-Enron.html provides a version of the network that is explicitly undirected. Origin Notes: Data made public by the Federal Energy Regulatory Commission when investigating Enron. It has had a few changes over time documented in the link. The data is currently hosted by William W. Cohen from CMU on a website, and it is also hosted on SNAP. SNAP asks to cite the paper linked in Origin paper. graph features handled: Dynamic, Dynamic (discrete), Generic, Large Graph features in papers: dynamic,dynamic (discrete),layered graphs,n-layers Origin Paper: Introducing the Enron Corpus (https://www.notion.so/Introducing-the-Enron-Corpus-52663d33d36b4940b5966f72f6c73a70?pvs=21) Originally found at: https://www.cs.cmu.edu/~enron/ Size: The data consists of 150 Enron executives, who sent 500,000 messages between themselves. Number of Graphs: 1 Child collections: SNAP (Stanford Network Analysis Platform) (SNAP%20(Stanford%20Network%20Analysis%20Platform)%201cd77eaee57147ce8263b2f9eaa2589c.md) Appeared in years: 2015,2013 Type of Collection: Subset of other collection is it stored properly?: No must be analyzed: Yes In repo?: No Related to Literature - Algorithm (1) (Dataset tag relations): Reordering massive sequence views: Enabling temporal and structural analysis of dynamic networks (https://www.notion.so/Reordering-massive-sequence-views-Enabling-temporal-and-structural-analysis-of-dynamic-networks-59101c28c7a54141bfebd74208f0250f?pvs=21), An Efficient Framework for Generating Storyline Visualizations from Streaming Data (https://www.notion.so/An-Efficient-Framework-for-Generating-Storyline-Visualizations-from-Streaming-Data-d06da0be3b5244b791f30c22ff2b7277?pvs=21) cleaned format?: No duplicate?: No link works?: Yes Added in paper: Yes Origin paper plaintext: Introducing the Enron Corpus Page id: ed3c62b92cf14a7b86c691ac3651dab3 unavailable/skip: Yes Cleaned ALL data: No first look: No Related to Literature - Algorithm (Dataset tag relations) 1: An Efficient Framework for Generating Storyline Visualizations from Streaming Data (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/An%20Efficient%20Framework%20for%20Generating%20Storyline%20Vi%200a3f35ebac3f468cb9c3adee03f62a06.md), Reordering massive sequence views: Enabling temporal and structural analysis of dynamic networks (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Reordering%20massive%20sequence%20views%20Enabling%20tempora%20f62118d5310d44b099b3ccc71fd5a8aa.md)
2 Body
Descriptions from Literature
From “Reordering massive sequence views: Enabling temporal and structural analysis of dynamic networks”:
We cleaned the data set by removing duplicates, spam and only to contain internal communication between Enron employees annotated with employee function leaving us with 151 nodes (employees) and 21374 edges (emails). Visualization using a node-link diagram enables the identification of stronger connections (see Figure 9(a)). However, temporal patterns and the evolution of the network cannot be analysed. From the standard MSV (Figure 9(b)) it becomes somewhat clear that transaction density increases over time and we can distinguish between different phases. We cannot, however, see features and identify communities due to visual clutter
Example Figures
From “An Efficient Framework for Generating Storyline Visualizations from Streaming Data”:
From “Reordering massive sequence views: Enabling temporal and structural analysis of dynamic networks”:
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https://www.cs.cmu.edu/~enron/
150 enron executives (users), 500k messages - many more employees
some have attachments
case nodes are users: could be a very connected dataset (perhaps too few nodes) - very high variance in node degree
case nodes are messages: could be a sparsely connected graph
there’s dates → can be layered
This dataset was collected and prepared by the
(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 some parse-able format like “Doe, John” or “Mary K. Smith”) and to no_address@enron.com when no recipient was specified.
Visualizations from https://www.kaggle.com/jamestollefson/enron-network-analysis:
This paper definitely uses this as a layered, categorical graph - could even be a hypergraph
→ unclear. Layered?