The evolution of graph structures in large time-varying graphs is often difficult to interpret due to excessive clutter from overlapping nodes and edges. On small displays, visual clutter often increases. This makes it difficult to recognize developing patterns in embedded subgraphs. In such situations, viewers are often hampered in observing and exploring significant changes of the graph components. This poses a cognitive barrier in the visual analytics of large dynamic structures. Another important problem in visualizing dynamic graphs is capturing the difference between graph states. Their state changes often become intractable. We propose to construct cognitive templates for grouping closely related entities using community detection techniques. The induced subgraphs are collapsed into meta–nodes in order to simplify the representation of large graphs and find similarities between communities. In order to compute the new structures, we introduce the Graph Convolution Network that learns the numeric representations of subgraphs induced by communities. The pair-wise similarities can then be calculated by graph–based cluster search algorithms. Furthermore, the proximity state might change temporally. In order to extract and match communities between consecutive snapshots we use multi-dimensional scaling and color mappings that reveal the evolution of graphs at the community level. We evaluate the effectiveness of our method by applying it to the Wikipedia edit history data set.
|Number of pages||17|
|Journal||International Journal of Cognitive Informatics and Natural Intelligence|
|Publication status||Published - Jun 2020|