具有组重叠的网络中节点组成员的检测
Detection of node group membership in networks with group overlap
摘要：
在社会和生物化学系统中发现的大多数网络都具有模块化结构。这些网络的模块性所引发的一个重要问题是，节点是否可以说属于一个单一的组。如果他们不能，我们就需要考虑“重叠社区”的作用。“尽管在这个方向上做出了一些努力，但检测重叠组的问题仍然没有解决，因为既没有重叠社区的正式定义，也没有一个网络集合，当节点可以属于多个组时，可以测试组检测算法的性能。在这里，我们介绍一个具有重叠组的网络集合。然后，我们应用三组识别方法-模块化最大化，k-团渗透，和模块化景观测量-这些网络。我们发现，模块化景观测量方法是唯一一个能够检测节点成员的异质性，这些异质性只有当重叠是小的检测。令人惊讶的是，我们发现，k-团渗流方法是无法检测节点成员重叠的情况下。
英文摘要：
Most networks found in social and biochemical systems have modular structures. An important question prompted by the modularity of these networks is whether nodes can be said to belong to a single group. If they cannot, we would need to consider the role of "overlapping communities." Despite some efforts in this direction, the problem of detecting overlapping groups remains unsolved because there is neither a formal definition of overlapping community, nor an ensemble of networks with which to test the performance of group detection algorithms when nodes can belong to more than one group. Here, we introduce an ensemble of networks with overlapping groups. We then apply three group identification methods--modularity maximization, k-clique percolation, and modularity-landscape surveying--to these networks. We find that the modularity-landscape surveying method is the only one able to detect heterogeneities in node memberships, and that those heterogeneities are only detectable when the overlap is small. Surprisingly, we find that the k-clique percolation method is unable to detect node membership for the overlapping case.
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