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3 results for “LastFM”
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>
lastfm Music Recommendation Dataset
<p>This is a common Zenodo repository for both<a href="http://ocelma.net/MusicRecommendationDataset/lastfm-360K.html"> lastfm-360K</a> and <a href="http://ocelma.net/MusicRecommendationDataset/lastfm-1K.html">lastfm-1K</a> datasets. See below the details of both datasets, including license, acknowledgements, contact, and instructions to cite.</p> <p> </p> <p><strong>LASTFM-360K (version 1.2, March 2010).</strong></p> <ul> <li><strong>What is this?</strong> This dataset contains <user, artist, plays> tuples (for ~360,000 users) collected from <a href="http://www.last.fm/api">Last.fm API</a>, using the <a href="http://www.last.fm/api/show?service=300">user.getTopArtists()</a> method.</li> <li><strong>Files:</strong> <ul> <li>usersha1-artmbid-artname-plays.tsv (MD5: be672526eb7c69495c27ad27803148f1)</li> <li>usersha1-profile.tsv (MD5: 51159d4edf6a92cb96f87768aa2be678)</li> <li>mbox_sha1sum.py (MD5: feb3485eace85f3ba62e324839e6ab39)</li> </ul> </li> <li><strong>Data Statistics:</strong> <ul> <li>File <em>usersha1-artmbid-artname-plays.tsv</em>: <ul> <li>Total Lines: 17,559,530</li> <li>Unique Users: 359,347</li> <li>Artists with <a href="http://musicbrainz.org/">MBID</a>: 186,642</li> <li>Artists without <a href="http://musicbrainz.org/">MBID</a>: 107,373</li> </ul> </li> </ul> </li> <li><strong>Data Format:</strong> The data is formatted one entry per line as follows (tab separated "\t"): <ul> <li>File <em>usersha1-artmbid-artname-plays.tsv</em>: <pre>user-mboxsha1 \t musicbrainz-artist-id \t artist-name \t plays</pre> </li> <li>File <em>usersha1-profile.tsv</em>: <pre>user-mboxsha1 \t gender (m|f|empty) \t age (int|empty) \t country (str|empty) \t signup (date|empty)</pre> </li> </ul> </li> <li><strong>Example:</strong> <ul> <li>File <em>usersha1-artmbid-artname-plays.tsv</em>: <pre>000063d3fe1cf2ba248b9e3c3f0334845a27a6be \t a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432 \t u2 \t 31 ...</pre> </li> <li>File <em>usersha1-profile.tsv</em>: <pre>000063d3fe1cf2ba248b9e3c3f0334845a27a6be \t m \t 19 \t Mexico \t Apr 28, 2008 ...</pre> </li> </ul> </li> </ul> <p> </p> <p><strong>LASTFM-1K (version 1.0, March 2010).</strong></p> <ul> <li><strong>What is this?</strong> This dataset contains <user, timestamp, artist, song> tuples collected from <a href="http://www.last.fm/api">Last.fm API</a>, using the <a href="http://www.last.fm/api/show?service=278">user.getRecentTracks()</a> method. This dataset represents the whole listening habits (till May, 5th 2009) for nearly 1,000 users.</li> <li><strong>Files:</strong> <ul> <li>userid-timestamp-artid-artname-traid-traname.tsv (MD5: 64747b21563e3d2aa95751e0ddc46b68)</li> <li>userid-profile.tsv (MD5: c53608b6b445db201098c1489ea497df)</li> </ul> </li> <li><strong>Data Statistics:</strong> <ul> <li>File <em>userid-timestamp-artid-artname-traid-traname.tsv:</em> <ul> <li>Total Lines: 19,150,868</li> <li>Unique Users: 992</li> <li>Artists with MBID: 107,528</li> <li>Artists without MBDID: 69,420</li> </ul> </li> </ul> </li> <li><strong>Data Format:</strong> The data is formatted one entry per line as follows (tab separated, "\t"): <ul> <li>File <em>userid-timestamp-artid-artname-traid-traname.tsv</em>: <pre>userid \t timestamp \t musicbrainz-artist-id \t artist-name \t musicbrainz-track-id \t track-name</pre> </li> <li>File <em>userid-profile.tsv</em>: <pre>userid \t gender ('m'|'f'|empty) \t age (int|empty) \t country (str|empty) \t signup (date|empty)</pre> </li> </ul> </li> <li><strong>Example:</strong> <ul> <li>File <em>userid-timestamp-artid-artname-traid-traname.tsv</em>: <pre>user_000639 \t 2009-04-08T01:57:47Z \t MBID \t The Dogs D'Amour \t MBID \t Fall in Love Again? user_000639 \t 2009-04-08T01:53:56Z \t MBID \t The Dogs D'Amour \t MBID \t Wait Until I'm Dead ...</pre> </li> <li>File <em>userid-profile.tsv</em>: <pre>user_000639 \t m \t Mexico \t Apr 27, 2005 ...</pre> </li> </ul> </li> </ul> <p> </p> <p><strong>LICENSE OF BOTH DATASETS</strong>. The data contained in both datasets is distributed with permission of <a href="http://last.fm">Last.fm</a>. The data is made available for non-commercial use. Those interested in using the data or web services in a commercial context should contact:</p> <p><em>partners [at] last [dot] fm</em></p> <p>For more information see Last.fm <a href="http://www.last.fm/api/tos">terms of service</a></p> <p> </p> <p><strong>ACKNOWLEDGEMENTS. </strong>Thanks to Last.fm for providing the access to this data via their web services. Special thanks to <a href="http://www.last.fm/user/nova77LF">Norman Casagrande</a>.</p> <p> </p> <p><strong>REFERENCES. </strong>When using this dataset you must reference the <a href="http://last.fm">Last.fm</a> webpage. Optionally (not mandatory at all!), you can cite <em>Chapter 3</em> of <a href="http://ocelma.net/MusicRecommendationBook/index.html">this book</a>:</p> <pre>@book{Celma:Springer2010, author = {Celma, O.}, title = {{Music Recommendation and Discovery in the Long Tail}}, publisher = {Springer}, year = {2010} } </pre> <p> </p> <p><strong>CONTACT: </strong>This data was collected by <a href="http://ocelma.net/">Òscar Celma</a> @ <a href="http://mtg.upf.edu">MTG</a>/<a href="http://upf.edu">UPF</a></p>
The "lastfm" data set used in the article "A comparative study of social network models: Network evolution models and nodal attribute models"
<p>This is the "lastfm" network used in the article:</p> <p>Toivonen, R., Kovanen, L., Kivelä, M., Onnela, J. P., Saramäki, J., & Kaski, K. (2009). A comparative study of social network models: Network evolution models and nodal attribute models. Social networks, 31(4), 240-254.</p> <p>doi:10.1016/j.socnet.2009.06.004</p> <p>The data set is described in the article. Please cite the original article when using this data set.</p> <p>Format of the data set is an edge list, where row in the file is an edge connecting the two nodes indicated by the two numbers separated by a whitespace. Each node number corresponds to a single account in the website.</p> <p>The original data in which this network is based on was licensed under the "Creative Commons Attribution-NonCommercial-ShareAlike 2.0 UK: England & Wales" licese, and accordinly this data set uses the same license. License available at https://creativecommons.org/licenses/by-nc/2.0/uk/</p>
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