We present an interactive Web platform that, given a directed graph, allows identifying the most relevant nodes related to a given query node. Besides well-established algorithms such as PageRank and Personalized PageRank, the demo includes Cyclerank, a novel algorithm that addresses some of their limitations by leveraging cyclic paths to compute personalized relevance scores. Our demo design enables two use cases: (a) algorithm comparison, comparing the results obtained with different algorithms, and (b) dataset comparison, for exploring and gaining insights into a dataset and comparing it with others. We provide 50 pre-loaded datasets from Wikipedia, Twitter, and Amazon and seven algorithms. Users can upload new datasets, and new algorithms can be easily added. By showcasing efficient algorithms to compute relevance scores in directed graphs, our tool helps to uncover hidden relationships within the data, which makes of it a valuable addition to the repertoire of graph analysis algorithms.

翻译：我们提出了一个交互式Web平台，该平台在给定有向图的情况下，能够识别与给定查询节点最相关的节点。除了PageRank和个性化PageRank等成熟算法外，该演示还包含Cyclerank算法——一种通过利用循环路径计算个性化相关性分数、从而解决现有算法某些局限性的新型方法。我们的演示设计支持两种应用场景：(a) 算法对比：比较不同算法获得的结果；(b) 数据集对比：用于探索和深入了解数据集，并将之与其他数据集进行比较。我们提供了来自维基百科、Twitter和亚马逊的50个预加载数据集以及七种算法。用户可以上传新数据集，新算法也可便捷地扩展添加。通过展示在有向图中高效计算相关性分数的算法，我们的工具有助于揭示数据中的隐含关系，使其成为图分析算法库中的重要补充工具。