Visually encoding quantitative information associated with graph links is an important problem in graph visualization. A conventional approach is to vary the thickness of lines to encode the strength of connections in node-link diagrams. In this paper, we present Sticky Links, a novel visual encoding method that draws graph links with stickiness. Taking the metaphor of links with glues, sticky links represent connection strength using spiky shapes, ranging from two broken spikes for weak connections to connected lines for strong connections. We conducted a controlled user study to compare the efficiency and aesthetic appeal of stickiness with conventional thickness encoding. Our results show that stickiness enables more effective and expressive quantitative encoding while maintaining the perception of node connectivity. Participants also found sticky links to be more aesthetic and less visually cluttering than conventional thickness encoding. Overall, our findings suggest that sticky links offer a promising alternative to conventional methods for encoding quantitative information in graphs.

The distributed acoustic sensing (DAS) technology that has emerged in recent years, combined with existing optical cables for earthquake monitoring, has the advantages of low deployment cost and dense sampling capability and has been used in various environments. However, the massive data generated by DAS are difficult to process manually, and there is an urgent need for efficient automatic earthquake detection algorithm. The deep neural networks trained with manually processed earthquake data in the past decade have been introduced. However, available manually processed earthquake DAS data are very limited, which makes it difficult to train neural networks. A convolutional neural network called “APM-DAS-earthquake network (ADE-Net) ” was built and trained with only five earthquakes’ records in Tangshan, China. The neural network detected about 2.56 times earthquakes on a DAS dataset than the permanent seismic network’s catalog with an accuracy rate of 80.4% and a recall rate of 93.18%. Results of tests with synthetic datasets and another real dataset suggest the neural network works well with a variety of earthquake location, magnitude, type, and array geometry. The computational time cost with a PC for 10-s data is less than 2.5-s data, which demonstrates its feasibility on time-sensitive applications, for example, earthquake early warning.