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|Title:||Clique Displacement: A New Layout Technique|
|Citation:||2019 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2019 - Proceedings , Vol. , , p. -|
|Abstract:||A graph allows the representation of data in a comprehensible way either for visualizing clusters in a large data set or analyzing trafc from network devices represented as nodes and links. It has always been a good mode for understanding a problem and subsequently analyzing the solution. A graph data structure is a good choice to represent data if it can be converted in the form of nodes and edges. There are different types of problems in various elds which require a graphical method to understand the problem, and so there are several approaches for doing it. On similar lines, this paper proposes a new visualization technique for a dense graph. It is evident that existing visualization techniques, such as force-directed placement would not give optimal results for a dense graph. The proposed work is interesting and has several potential applications such as in analyzing graphs for Social Networks, Biological applications, etc. There are many graph layout techniques available, and it has been studied for years. Every year some new method is proposed, or an older one is improved. This is because of the exponentially increasing data, which requires a better layout technique for representation. Today, graph is used in different research areas due to its simplicity in the way of representing the information. A graphical representation is always good for understanding the information easily. When these graphs become dense, the overlapping of edges in the graph also increases by a certain amount, and it becomes difcult to understand the graph or tough to visualize the graph. For a better understanding of dense graph, this paper proposes a solution by dividing the original graph into sub-graphs forming cliques. These cliques are then aligned in special positions to overcome the confusion between the connectivity of nodes. This idea considerably solves the visualization problem with dense networks, and the results show a better visual representation. © 2019 IEEE.|
|Appears in Collections:||2. Conference Papers|
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