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3 results for “Enron”
Enron Email Time-Series Network
<p>We use the <a href="https://www.kaggle.com/wcukierski/enron-email-dataset">Enron email dataset</a> to build a network of email addresses. It contains 614586 emails sent over the period from 6 January 1998 until 4 February 2004. During the pre-processing, we remove the periods of low activity and keep the emails from 1 January 1999 until 31 July 2002 which is 1448 days of email records in total. Also, we remove email addresses that sent less than three emails over that period. In total, the Enron email network contains 6 600 nodes and 50 897 edges.</p> <p>To build a graph <em>G = (V</em><em>, E</em><em>)</em>, we use email addresses as nodes <em>V</em>. Every node <em>v<sub>i</sub></em> has an attribute which is a time-varying signal that corresponds to the number of emails sent from this address during a day. We draw an edge <em>e</em><em><sub><em>ij</em></sub></em> between two nodes <em>i</em> and <em>j</em> if there is at least one email exchange between the corresponding addresses.</p> <p>Column <em>'Count'</em> in <em>'edges.csv'</em> file is the number of 'From'->'To' email exchanges between the two addresses. This column can be used as an edge weight.</p> <p>The file <em>'nodes.csv'</em> contains a dictionary that is a compressed representation of time-series. The format of the dictionary is <em>Day->The Number Of Emails Sent By the Address During That Day.</em> The total number of days is 1448.</p> <p><em>'id-email.csv'</em> is a file containing the actual email addresses.</p>
email-Enron
<h3><strong>Overview</strong></h3><p>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.</p><p>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 This data was originally made public and posted to the web by the Federal Energy Regulatory Commission during its investigation.</p><p>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 <a href="mailto:user@enron.com">user@enron.com</a> whenever possible (i.e., the recipient is specified in some parseable format like "Doe, John" or "Mary K. Smith") and to <a href="mailto:no_address@enron.com">no_address@enron.com</a> when no recipient was specified.</p><h4><strong>Statistics</strong></h4><p>Some basic statistics of this dataset are:</p><ul><li>number of nodes: 148</li><li>number of timestamped hyperedges: 10,885</li><li>distribution of the connected components:</li></ul><p>Component Size, Number </p><ul><li>143, 1</li><li>1, 5</li></ul><h4><strong>Source of original data</strong></h4><p>Source: <a href="https://www.cs.cornell.edu/~arb/data/email-Enron/">email-Enron dataset</a></p><h4><strong>References</strong></h4><p>If you use this dataset, please cite these references:</p><ul><li><a href="https://doi.org/10.1073/pnas.1800683115">Simplicial closure and higher-order link prediction</a>. Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg. Proceedings of the National Academy of Sciences (PNAS), 2018.</li><li><a href="https://www.cs.cmu.edu/~enron/">Enron Email Dataset</a>, William Cohen, 2015.</li></ul>
Two Dynamic Attributed Networks: Enron & Jazz LastFM
<p><strong>Description. </strong>This repository contains two dynamic and attributed social networks extracted from the well-known Enron email dataset, and from the LastFM online music platform. We used both networks in the following papers:</p> <ol> <li>G. K. Orman, V. Labatut, M. Plantevit, and J.-F. Boulicaut, “A Method for Characterizing Communities in Dynamic Attributed Complex Networks,” in <em>IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM)</em>, 2014, pp. 481–484. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01011913">hal-01011913</a>⟩ DOI: <a href="http://doi.org/10.1109/ASONAM.2014.6921629">10.1109/ASONAM.2014.6921629</a></li> <li>G. K. Orman, V. Labatut, M. Plantevit, and J.-F. Boulicaut, “Interpreting communities based on the evolution of a dynamic attributed network,” <em>Social Network Analysis and Mining</em>, vol. 5, p. 20, 2015. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01163778">hal-01163778</a>⟩ DOI: <a href="http://doi.org/10.1007/s13278-015-0262-4">10.1007/s13278-015-0262-4</a></li> </ol> <p><strong>Citation. </strong>If you use these data, please cite the paper [1].</p> <p><br><code>@InProceedings{Orman2014,</code><br><code> author = {Orman, Günce Keziban and Labatut, Vincent and Plantevit, Marc and Boulicaut, Jean-François},</code><br><code> title = {A Method for Characterizing Communities in Dynamic Attributed Complex Networks},</code><br><code> booktitle = {IEEE/ACM International Conference on Advances in Social Network Analysis and Mining},</code><br><code> year = {2014},</code><br><code> pages = {481-484},</code><br><code> address = {Beijing, CN},</code><br><code> publisher = {IEEE Publishing},</code><br><code> doi = {10.1109/ASONAM.2014.6921629},</code><br><code>}</code></p> <p>----------------------------------------</p> <p><strong>Enron dataset. </strong>Enron is a well-known dataset in network science and text mining. It has been widely studied in academia. In network science, several different static networks appear in the literature. However, up to now, no dynamic network has been published, even though the email conversations have timestamps.</p> <p>We processed the original dataset to extract a dynamic network. There are 158 nodes representing Enron employees between 1997 and 2002. All the addresses in the <em>From</em> and <em>To</em> fields of each email are considered, resulting in a network of 28,802 nodes representing a distinct email addresses. A time span of one month is chosen for the time slices, generating 46 time slices. Two nodes are connected if the corresponding persons emailed each other during the given time slice. We did not make any distinction between sender and receiver, and thus produced an undirected dynamic network. </p> <p>----------------------------------------</p> <p><strong>LastFM dataset. </strong>LastFM is a music website that allows its members to register and listen to music online. It is also a social network platform, because its members can declare friendship relationships. In LastFM, members can join a predefined group related to their music tastes, and participate in music-related events such as concerts. Using the LastFM API, One can retrieve the information of the artist and track a user has listened to, with the exact timestamp. Moreover, it is also possible to get some information regarding the music-related events the users joined, including the exact timestamps.</p> <p>We extracted a network by focusing on the members of the <em>Jazz</em> group, which is supposed to include users appreciating this type of music. We took advantage of the LastFM API to retrieve the members of this group and the existing friendship connection between them. In the end, our network contains 1,702 nodes representing the <em>Jazz</em> users. The friendship relationships between them is static, though, in the sense that the LastFM API does not give access to any temporal information regarding their beginning or end. So, we decided to take advantage of some additional information to get a dynamic structure. We put a link between two nodes if two conditions were simultaneously true: 1) both considered users listened to at least one common artist for a specific period of time, and 2) they are friends on the LastFM platform. For the mentioned period of time, we decided to use 3 months with 1 month overlap, after having analyzed the dynamics of the platform. In other words, we extracted a dynamic network in which each time slice represents three months of LastFM usage for our 1,702 users of interest. There are one month overlap between two consecutive time slices.</p>
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