Local detection of communities by an analogy to memory recall in the brain

In network science a 'community' refers to a group of nodes in a network that are densely connected within this group but are less connected with nodes outside this group. Community structure is a hallmark of a variety of social, biological and engineering networks, and development of effective and efficient methods for detecting communities in networks has been a central issue of network science. We put forward a method of local detection of communities inspired by the cell assembly hypothesis, which has been longstanding and prevailing in neuroscience. The hypothesis states that: Neurons coding a specific item tend to be mutually connected, thus forming a 'cell assembly'; memory recall of the item is associated with reverberative activation of neurons comprising the cell assembly. Here we compare communities to cell assemblies and propose to detect communities in networks by use of the dynamics modeling neural mechanisms for memory recall in the brain. Community detection from benchmark networks by the proposed method is perfect or highly accurate. These results demonstrate the validity of local detection of communities that is processed in an analogy to memory recall in the brain.