This paper introduces the subgraph nomination inference task, in which example subgraphs of interest are used to query a network for similarly interesting subgraphs. This type of problem appears time and again in real world problems connected to, for example, user recommendation systems and structural retrieval tasks in social and biological/connectomic networks. We formally define the subgraph nomination framework with an emphasis on the notion of a user-in-the-loop in the subgraph nomination pipeline. In this setting, a user can provide additional post-nomination light supervision that can be incorporated into the retrieval task. After introducing and formalizing the retrieval task, we examine the nuanced effect that user-supervision can have on performance, both analytically and across real and simulated data examples.
翻译:本文介绍子图提名的推论任务,其中以感兴趣的子集为例,查询类似有趣的子集的网络。这类问题在现实世界中反复出现,例如,与社会网络和生物/连结网中的用户推荐系统和结构检索任务有关。我们正式定义子图提名框架,强调子图提名管道中的用户在插图中的概念。在这一背景下,用户可以提供额外的提名后光监督,这可以纳入检索任务。在引入检索任务并将其正规化后,我们研究用户监督对实际和模拟数据实例的性能的细微影响。