Isolation is a concept originally conceived in the context of clique enumeration in static networks, mostly used to model communities that do not have much contact to the outside world. Herein, a clique is considered isolated if it has few edges connecting it to the rest of the graph. Motivated by recent work on enumerating cliques in temporal networks, we transform the isolation concept to the temporal setting. We discover that the addition of the time dimension leads to six distinct natural isolation concepts. Our main contribution is the development of parameterized enumeration algorithms for five of these six isolation types for clique enumeration, employing the parameter “degree of isolation.” In a nutshell, this means that the more isolated these cliques are, the faster we can find them. On the empirical side, we implemented and tested these algorithms on (temporal) social network data, obtaining encouraging results.

中文翻译：

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应用于时间团枚举的隔离概念

隔离最初是在静态网络中的集团枚举背景下构思的一个概念，主要用于对与外界没有太多联系的社区进行建模。这里考虑一个集团孤立如果它有很少的边将它连接到图的其余部分。受最近在时间网络中枚举集团的工作的启发，我们将隔离概念转换为时间设置。我们发现时间维度的添加导致了六个不同的自然隔离概念。我们的主要贡献是为集团枚举的这六种隔离类型中的五种开发了参数化枚举算法，使用参数“隔离度”。简而言之，这意味着这些派系越孤立，我们就能越快找到它们。在经验方面，我们在（时间）社交网络数据上实施和测试了这些算法，获得了令人鼓舞的结果。