%0 Journal Article %1 HuangHuang15 %A Huang, Xiaodi %A Huang, Weidong %D 2015 %J Journal of Visual Languages & Computing %K citas, citeulike clustering, graphs, visualization %P 71--82 %R 10.1016/j.jvlc.2014.12.007 %T GO: A cluster algorithm for graph visualization %U http://dx.doi.org/10.1016/j.jvlc.2014.12.007 %V 28 %X We present an algorithm for clustering large graphs. The algorithm is able to detect the number of clusters automatically and the clusters with the suitable sizes in real- time from a given graph. The algorithm performs better than the compared algorithms against the real-world graphs. As we are in the big data age, graph data such as user networks in Facebook and Flickr becomes large. How to reduce the visual complexity of a graph layout is a challenging problem. Clustering graphs is regarded as one of effective ways to address this problem. Most of current graph visualization systems, however, directly use existing clustering algorithms that are not originally developed for the visualization purpose. For graph visualization, a clustering algorithm should meet specific requirements such as the sufficient size of clusters, and automatic determination of the number of clusters. After identifying the requirements of clustering graphs for visualization, in this paper we present a new clustering algorithm that is particularly designed for visualization so as to reduce the visual complexity of a layout, together with a strategy for improving the scalability of our algorithm. Experiments have demonstrated that our proposed algorithm is capable of detecting clusters in a way that is required in graph visualization.

@article{HuangHuang15, abstract = {{ We present an algorithm for clustering large graphs. The algorithm is able to detect the number of clusters automatically and the clusters with the suitable sizes in real- time from a given graph. The algorithm performs better than the compared algorithms against the real-world graphs. As we are in the big data age, graph data such as user networks in Facebook and Flickr becomes large. How to reduce the visual complexity of a graph layout is a challenging problem. Clustering graphs is regarded as one of effective ways to address this problem. Most of current graph visualization systems, however, directly use existing clustering algorithms that are not originally developed for the visualization purpose. For graph visualization, a clustering algorithm should meet specific requirements such as the sufficient size of clusters, and automatic determination of the number of clusters. After identifying the requirements of clustering graphs for visualization, in this paper we present a new clustering algorithm that is particularly designed for visualization so as to reduce the visual complexity of a layout, together with a strategy for improving the scalability of our algorithm. Experiments have demonstrated that our proposed algorithm is capable of detecting clusters in a way that is required in graph visualization.}}, added-at = {2017-09-08T10:52:59.000+0200}, author = {Huang, Xiaodi and Huang, Weidong}, biburl = {https://www.bibsonomy.org/bibtex/226d4a80e6b219b16d0adfd34b309a363/fernand0}, citeulike-article-id = {13496506}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.jvlc.2014.12.007}, doi = {10.1016/j.jvlc.2014.12.007}, interhash = {4b78a1ca4a847684ff55d1df25a08500}, intrahash = {26d4a80e6b219b16d0adfd34b309a363}, issn = {1045926X}, journal = {Journal of Visual Languages \& Computing}, keywords = {citas, citeulike clustering, graphs, visualization}, month = jun, pages = {71--82}, posted-at = {2015-01-20 15:22:49}, priority = {2}, timestamp = {2017-09-08T10:53:23.000+0200}, title = {{GO: A cluster algorithm for graph visualization}}, url = {http://dx.doi.org/10.1016/j.jvlc.2014.12.007}, volume = 28, year = 2015 }