Tag community detection and recommendation
A Java project for community detection and recommendation.
This is a Java project implementing the framework for tag community detection method presented by Papadopoulos et al. in DaWaK 2010 (see references below for full citation). In addition, it contains several utility methods for performing tag recommendation and evaluating the method.
1. Download the rar archive here.
2. Unzip the archive in your Java workspace. You can directly import the folder as an Eclipse project.
3. Make sure that you include all libraries in the lib folder in your class path.
4. Make sure that you make appropriate modifications to the class dawak2010.Config in order to point to the appropriate local paths.
5. You may run the class dawak2010.DaWak2010 to repeat the experiments reported in the paper.
6. The package graph.clust contains several variants of clustering algorithms that can be used stand-alone.
7. The aforementioned rar archive only contains the BIBSONOMY-200K dataset. If you are interested in experimented with the FLICKR-1M and DELICIOUS-7M datasets reported in the paper, download the following files, FLICKR-1M and DELICIOUS-7M, and extract them following a similar folder structure as BIBSONOMY-200K.
If you make use of this software for your research work, please cite one or both of the following papers:
S. Papadopoulos, Y. Kompatsiaris, A. Vakali. “A Graph-based Clustering Scheme for Identifying Related Tags in Folksonomies”. In Proceedings of DaWaK'10, 12th International Conference on Data Warehousing and Knowledge discovery (Bilbao, Spain), Springer-Verlag, 65-76
S. Papadopoulos, A. Vakali, Y. Kompatsiaris. “Community Detection in Collaborative Tagging Systems”. In Book Community-built Database: Research and Development, Springer, 2011
The source code included in this project is provided under the GNU General Public License, Version 3.
This software is provided by the author "as is" and any express or implied warranties, including, but not limited to the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the author be liable for and direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services, loss of use, data, or profits, or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.
This work was supported by the EU FP7 project WeKnowIt (FP7-215453).
You may contact Symeon Papadopoulos (papadop AT iti DOT gr) for any question or remark you may have with respect to the code.