title: Think locally, act locally: detection of small, medium-sized, and large communities in large networks.
abstract: It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally biased methods that focus on communities that are centered around a given seed node (or set of seed nodes) might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate structural properties that are important to consider in the development of better benchmark networks to test methods for community detection.
authors: Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom.
authors: Lucas G S
authors: LG
authors: Jeub
authors: Morgan Stanley, Montreal, Quebec, H3C 3S4, Canada and Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215, USA.
authors: Prakash
authors: P
authors: Balachandran
authors: Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom and CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, United Kingdom.
authors: Mason A
authors: MA
authors: Porter
authors: Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599-3250, USA.
authors: Peter J
authors: PJ
authors: Mucha
authors: International Computer Science Institute, Berkeley, California 94704, USA and Department of Statistics, University of California at Berkeley, Berkeley, California 94720, USA.
authors: Michael W
authors: MW
authors: Mahoney
mesh_terms: D004058:Diffusion; D008962:Models, Theoretical