Okamoto Hiroshi
Research & Technology Group, Fuji Xerox Co., Ltd., Kanagawa, Japan; RIKEN Brain Science Institute, Saitama, Japan.
Biosystems. 2016 Aug;146:85-90. doi: 10.1016/j.biosystems.2016.03.006. Epub 2016 Mar 24.
Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of "true" members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., "false" members of the community). We propose to detect the community from this "imperfect" and "inaccurate" set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known.
网络中紧密相连的部分被称为“社区”。社区结构是各种现实世界网络的一个标志。网络中的各个社区构成了由网络描述的复杂系统的功能模块。因此，在网络中寻找社区对于理解和认识由网络描述的复杂系统至关重要。事实上，网络科学已经付出了巨大努力来开发有效且高效的网络社区检测方法。在此，我们提出一种社区检测方法，该方法迄今很少被研究，但具有实际应用价值。假设我们有一组源节点，其中包含特定社区的一些（但不是全部）“真实”成员；还假设该集合包含一些不属于这个社区的节点（即社区的“虚假”成员）。我们提议使用递归神经网络的吸引子动力学从这个“不完美”且“不准确”的源节点集合中检测社区。通过所提方法进行的社区检测可被视为从退化模式恢复原始模式，这类似于大脑中线索触发的短期记忆回忆。我们使用已知正确社区的合成网络和真实社交网络来证明所提方法的有效性。
