Link communities reveal multiscale complexity in networks

Abstract

Networks have become a key approach to understanding systems of interacting objects, unifying the study of diverse phenomena including biological organisms and human society. One crucial step when studying the structure and dynamics of networks is to identify communities: groups of related nodes that correspond to functional subunits such as protein complexes or social spheres. Communities in networks often overlap such that nodes simultaneously belong to several groups. Meanwhile, many networks are known to possess hierarchical organization, where communities are recursively grouped into a hierarchical structure. However, the fact that many real networks have communities with pervasive overlap, where each and every node belongs to more than one group, has the consequence that a global hierarchy of nodes cannot capture the relationships between overlapping groups. Here we reinvent communities as groups of links rather than nodes and show that this unorthodox approach successfully reconciles the antagonistic organizing principles of overlapping communities and hierarchy. In contrast to the existing literature, which has entirely focused on grouping nodes, link communities naturally incorporate overlap while revealing hierarchical organization. We find relevant link communities in many networks, including major biological networks such as protein-protein interaction and metabolic networks, and show that a large social network contains hierarchically organized community structures spanning inner-city to regional scales while maintaining pervasive overlap. Our results imply that link communities are fundamental building blocks that reveal overlap and hierarchical organization in networks to be two aspects of the same phenomenon.

Monitoring of Green Technology Using GTNet Membership and Published Information in Korea

“…Many researchers in order to understand the complex systems that are interrelated network theory was used [3]- [7]. There are many ongoing researches to detect the patterns of relationships (commonly known as communities) within the network based on the network theory [8]- [10]. Normally, networks tend to have a few clusters and as Fig.…”

A Novel Genetic Algorithm for Overlapping Community Detection

“…Based on the thought that each edge plays an unique role in the network, Ahn, Bagrow and Lehmann [8] first propose a link-based algorithm, clustering the edges of the network. They define the similarity of edges and an evaluation function for link community, partition density D. The algorithm first calculates the similarity of all edges of the network and assign each edge to its own community.…”

“…The node-based overlapping community detection algorithms [1,2,3,4,5,6,7,9], classify nodes of the network directly. The link-based algorithms cluster the edges of network, and map the final link communities to node communities by simply gather nodes incident to all edges within each link communities [8]. All of these algorithms contribute to overlapping community detection, however, they still have disadvantages.…”

“…The algorithm first finds the link communities by optimizing objective function partition density D [8], and then map the link communities to node communities based on a novel genotype representation method. The number of the communities found by GaoCD can be automatically determined, without any prior information.…”

“…Experiment on artificial networks shows that GaoCD work well on networks with typical overlapping structure. Experiments on real networks compare GaoCD with ABL [8] and GA-NET+ [9]. It is shown that GaoCD always achieves higher partition density D and finds denser communities.…”