When sharing information, a common tactic for reducing the cost of choosing recipients is to form named groups of users. These groups are then selected as recipients in lieu of or in addition to users. However, keeping named groups up to date is a difficult and error-prone task when conducted manually. Past schemes automating this task make different tradeoffs and can be distinguished based on several factors including the types of named groups they consider, whether they evolve a specific group or a set of multiple groups, and how integrated they are with techniques for predicting initial groups. We analyze these approaches and identify a design space of potential evolutionary approaches. Using this analysis, we introduce a novel approach for automatically suggesting a sub-type of evolution, evolutionary growth. This approach (a) requires no prior knowledge of which groups change, (b) composes, and therefore interoperates, with an existing engine for recommending named groups, and (c) extracts groups from the social graph of multiple types of applications regardless of whether the graph are explicit or derived implicitly from message communication. Our evaluation considers social graphs created using explicit and implicit connections, and identifies the conditions under which the approach outperforms baseline techniques.

中文翻译：

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迈向进化命名小组建议

共享信息时，减少选择收件人的成本的一种常见策略是组建命名组的用户。然后，选择这些组作为代替用户或除了用户之外的收件人。但是，手动执行时，使命名组保持最新是一项困难且容易出错的任务。过去使该任务自动化的方案会做出不同的权衡，并且可以基于几个因素进行区分，这些因素包括他们考虑的命名组的类型，是否演化一个特定的组或一组多个组，以及它们与预测初始组的技术如何集成。我们分析这些方法并确定潜在的进化方法的设计空间。使用此分析，我们介绍了一种新颖的方法，用于自动建议进化的子类型，即进化增长。这种方法（a）不需要先了解哪些组会发生变化，（b）会组成并因此进行互操作，（c）从多种类型的应用程序的社交图中提取组，而不管该图是显式的还是从消息通信中隐式派生的。我们的评估考虑了使用显式和隐式连接创建的社交图，并确定了该方法优于基线技术的条件。