Description. 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:

1. G. K. Orman, V. Labatut, M. Plantevit, and J.-F. Boulicaut, “A Method for Characterizing Communities in Dynamic Attributed Complex Networks,” in IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), 2014, pp. 481–484. ⟨hal-01011913⟩ DOI: 10.1109/ASONAM.2014.6921629
2. G. K. Orman, V. Labatut, M. Plantevit, and J.-F. Boulicaut, “Interpreting communities based on the evolution of a dynamic attributed network,” Social Network Analysis and Mining, vol. 5, p. 20, 2015. ⟨hal-01163778⟩ DOI: 10.1007/s13278-015-0262-4

Citation. If you use these data, please cite the paper [1].

@InProceedings{Orman2014,
author = {Orman, Günce Keziban and Labatut, Vincent and Plantevit, Marc and Boulicaut, Jean-François},
title = {A Method for Characterizing Communities in Dynamic Attributed Complex Networks},
booktitle = {IEEE/ACM International Conference on Advances in Social Network Analysis and Mining},
year = {2014},
pages = {481-484},
address = {Beijing, CN},
publisher = {IEEE Publishing},
doi = {10.1109/ASONAM.2014.6921629},
}

----------------------------------------

Enron dataset. 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.

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 From and To 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.

----------------------------------------

LastFM dataset. 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.

We extracted a network by focusing on the members of the Jazz 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 Jazz 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.

CPD KS framework performance summary.