Research On Visualization Of Entity Relationship Based On Graph

Entity-relationship data based on graph widely exists in life, such as social networks, citations networks, road transport networks, the Internet networks, wireless sensor networks and so on. These data are organized in the form of graph, referred to as graph data. How to mine useful information from these graph data is an important issue of entity-relationship data visualization.There are a variety of methods to mine useful information from graph data. Visualization is a typical way to mine information contained in graph data by giving user intuitive feelings. There are already a number of visualization algorithms. However, most of these algorithms only consider the complexity and aesthetics, which are difficult to meet user's new requirements.To address the above problems, the paper presents several visualization algorithms by in-depth study of state-of-the-art algorithms. First, the paper presents an interactivity-oriented progressive layout algorithm, which provides a good interactivity and high space utilization. The algorithms performs efficiently and exhibits an elegant layout result. In practice, users usually focus on some key nodes. Therefore, the paper then studies a key-node-oriented ring layout algorithm. According to different needs, the algorithm can be divided into two algorithms: ring layout based on force-directed algorithm and ring layout based on Genetic Algorithms. The former explores the clustering effect of graph data, and the latter puts nodes on the ring evenly. Both of the algorithms shows a beautiful layout result. To address the space utilization issues, this paper presents a clustering-oriented grid layout algorithm which has high space utilization and clustering effect.Finally, based on the above algorithms, this paper implements a visualization prototype system of entity-relationship based on graph. The system not only implements algorithms proposed in this paper as well as traditional layout algorithms, but also provides users with other relevant interaction abilities.