WebMILE: Democratizing Network Representation Learning at Scale
WebMILE: Democratizing Network Representation Learning at Scale
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DOI：
10.14778/3554821.3554883
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Yuntian He;Yue Zhang-;Srinivas Parthasarathy
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作者：
Yuntian He;Yue Zhang-;Srinivas Parthasarathy
近年来，我们已经看到网络表征学习（NRL）方法在不同领域的成功，从计算化学到药物发现，从社会网络分析到生物信息学算法。然而，每个这样的NRL方法通常在开发人员熟悉的编程环境中原型化。此外，这些方法很少扩展到大规模网络或图。这样的限制对于领域科学家或最终用户来说是有问题的，他们希望在他们特定领域的大型图上扩展特定的NRL方法。在这项工作中，我们提出了一个新的系统，WebMILE民主化这个过程。WebMILE可以在大型图形上扩展以用户首选编程语言编写的无监督网络嵌入方法。它为最终用户提供了一个易于使用的图形用户界面（GUI）。用户通过简单的GUI提供必要的输入（嵌入方法文件、图形、所需的包信息），WebMILE在给定的输入图形上执行输入网络嵌入方法。WebMILE利用了一种开创性的多级方法MILE（如果用户可以访问集群，则可以选择DistMILE），可以在大型图上扩展网络嵌入方法。语言不可知性是通过一个简单的Docker接口实现的。在本演示中，我们将展示领域科学家或最终用户如何利用WebMILE以灵活有效的方式快速构建原型并学习大型图的节点嵌入-确保高生产力和高性能的双重目标。
In recent years, we have seen the success of network representation learning (NRL) methods in diverse domains ranging from computational chemistry to drug discovery and from social network analysis to bioinformatics algorithms. However, each such NRL method is typically prototyped in a programming environment familiar to the developer. Moreover, such methods rarely scale out to large-scale networks or graphs. Such restrictions are problematic to domain scientists or end-users who want to scale a particular NRL method-of-interest on large graphs from their specific domain. In this work, we present a novel system, WebMILE to democratize this process. WebMILE can scale an unsupervised network embedding method written in the user's preferred programming language on large graphs. It provides an easy-to-use Graphical User Interface (GUI) for the end-user. The user provides the necessary input (embedding method file, graph, required packages information) through a simple GUI, and WebMILE executes the input network embedding method on the given input graph. WebMILE leverages a pioneering multi-level method, MILE (alternatively DistMILE if the user has access to a cluster), that can scale a network embedding method on large graphs. The language agnosticity is achieved through a simple Docker interface. In this demonstration, we will showcase how a domain scientist or end-user can utilize WebMILE to rapidly prototype and learn node embeddings of a large graph in a flexible and efficient manner - ensuring the twin goals of high productivity and high performance.