We introduce pygrank, an open source Python package to define, run and evaluate node ranking algorithms. We provide object-oriented and extensively unit-tested algorithm components, such as graph filters, post-processors, measures, benchmarks and online tuning. Computations can be delegated to numpy, tensorflow or pytorch backends and fit in back-propagation pipelines. Classes can be combined to define interoperable complex algorithms. Within the context of this paper we compare the package with related alternatives and demonstrate its flexibility and ease of use with code examples.
翻译:我们引入了开源 Pygrank 软件包, 用于定义、 运行和评估节点排序算法。 我们提供面向对象且经过广泛单位测试的算法组件, 如图形过滤器、 后处理器、 措施、 基准和在线调试。 计算可以下放到 numpy 、 高龙流 或 Pytoch 后端, 并适合后推进管道 。 分类可以合并来定义可互操作的复杂算法 。 在本文的背景下, 我们比较该软件包与相关替代软件, 并用代码示例展示其灵活性和使用方便性 。