Bipartite graphs are typically visualized using linked lists or matrices, but these visualizations neither scale well nor do they convey temporal development. We present a new interactive exploration interface for large, time-dependent bipartite graphs. We use two clustering techniques to build a hierarchical aggregation supporting different exploration strategies. Aggregated nodes and edges are visualized as linked lists with nested time series. We demonstrate two use cases: finding advertising expenses of public authorities following similar temporal patterns and comparing author-keyword co-occurrences across time. Through a user study, we show that linked lists with hierarchical aggregation lead to more insights than without.

二部图通常使用链接列表或矩阵来可视化，但是这些可视化既不能很好地缩放，也不能传达时间的发展。我们为大型的，与时间有关的二部图提供了一个新的交互式探索界面。我们使用两种聚类技术来构建支持不同探索策略的分层聚合。聚合的节点和边可视化为具有嵌套时间序列的链表。我们演示了两个用例：按照类似的时间模式查找公共机构的广告费用，并比较跨时间的作者-关键字共现。通过用户研究，我们发现具有分层聚合的链表比没有链结的列表能带来更多的见解。