Itoh Takayuki, Klein Karsten
IEEE Comput Graph Appl. 2015 Nov-Dec;35(6):30-40. doi: 10.1109/MCG.2015.115. Epub 2015 Sep 23.
Many graph-drawing methods apply node-clustering techniques based on the density of edges to find tightly connected subgraphs and then hierarchically visualize the clustered graphs. However, users may want to focus on important nodes and their connections to groups of other nodes for some applications. For this purpose, it is effective to separately visualize the key nodes detected based on adjacency and attributes of the nodes. This article presents a graph visualization technique for attribute-embedded graphs that applies a graph-clustering algorithm that accounts for the combination of connections and attributes. The graph clustering step divides the nodes according to the commonality of connected nodes and similarity of feature value vectors. It then calculates the distances between arbitrary pairs of clusters according to the number of connecting edges and the similarity of feature value vectors and finally places the clusters based on the distances. Consequently, the technique separates important nodes that have connections to multiple large clusters and improves the visibility of such nodes' connections. To test this technique, this article presents examples with human relationship graph datasets, including a coauthorship and Twitter communication network dataset.
许多图形绘制方法基于边的密度应用节点聚类技术来找到紧密连接的子图，然后分层可视化聚类后的图形。然而，对于某些应用，用户可能希望关注重要节点及其与其他节点组的连接。为此，分别可视化基于节点邻接性和属性检测到的关键节点是有效的。本文提出了一种针对属性嵌入图的图形可视化技术，该技术应用了一种考虑连接和属性组合的图聚类算法。图聚类步骤根据连接节点的共性和特征值向量的相似性对节点进行划分。然后根据连接边的数量和特征值向量的相似性计算任意两个聚类之间的距离，最后根据这些距离放置聚类。因此，该技术分离出与多个大聚类有连接的重要节点，并提高了此类节点连接的可视性。为了测试该技术，本文给出了人际关系图数据集的示例，包括合作作者和推特通信网络数据集。
