On embedding a graph in the grid with the minimum number of bends
SIAM Journal on Computing
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Self-organizing maps
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Drawing clusters and hierarchies
Drawing graphs
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Multilevel Visualization of Clustered Graphs
GD '96 Proceedings of the Symposium on Graph Drawing
Validation indices for graph clustering
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Adding filtering to geometric distortion to visualize a clustered graph on small screens
APVis '04 Proceedings of the 2004 Australasian symposium on Information Visualisation - Volume 35
Visual Analysis of Large Heterogeneous Social Networks by Semantic and Structural Abstraction
IEEE Transactions on Visualization and Computer Graphics
Engineering graph clustering: Models and experimental evaluation
Journal of Experimental Algorithmics (JEA)
Graph Visualization Techniques for Web Clustering Engines
IEEE Transactions on Visualization and Computer Graphics
How to Draw ClusteredWeighted Graphs using a Multilevel Force-Directed Graph Drawing Algorithm
IV '07 Proceedings of the 11th International Conference Information Visualization
Community detection in large-scale social networks
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Glimmer: Multilevel MDS on the GPU
IEEE Transactions on Visualization and Computer Graphics
Overlapped community detection in complex networks
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Agglomerative genetic algorithm for clustering in social networks
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Mining communities in networks: a solution for consistency and its evaluation
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
On the mean accuracy of statistical pattern recognizers
IEEE Transactions on Information Theory
Integrating heterogeneous information within a social network for detecting communities
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Due to the explosion of social networking and the information sharing among their users, the interest in analyzing social networks has increased over the recent years. Two general interests in this kind of studies are community detection and visualization. In the first case, most of the classic algorithms for community detection use only the structural information to identify groups, that is, how clusters are formed according to the topology of the relationships. However, these methods do not take into account any semantic information which could guide the clustering process, and which may add elements to conduct further analyses. In the second case most of the layout algorithms for clustered graphs have been designed to differentiate the groups within the graph, but they are not designed to analyze the interactions between such groups. Identifying these interactions gives an insight into the way different communities exchange messages or information, and allows the social network researcher to identify key actors, roles, and paths from one community to another. This article presents a novel model to use, in a conjoint way, the semantic information from the social network and its structural information to, first, find structurally and semantically related groups of nodes, and second, a layout algorithm for clustered graphs which divides the nodes into two types, one for nodes with edges connecting other communities and another with nodes connecting nodes only within their own community. With this division the visualization tool focuses on the connections between groups facilitating deep studies of augmented social networks.