18 research outputs found
Different approaches to community detection
A precise definition of what constitutes a community in networks has remained
elusive. Consequently, network scientists have compared community detection
algorithms on benchmark networks with a particular form of community structure
and classified them based on the mathematical techniques they employ. However,
this comparison can be misleading because apparent similarities in their
mathematical machinery can disguise different reasons for why we would want to
employ community detection in the first place. Here we provide a focused review
of these different motivations that underpin community detection. This
problem-driven classification is useful in applied network science, where it is
important to select an appropriate algorithm for the given purpose. Moreover,
highlighting the different approaches to community detection also delineates
the many lines of research and points out open directions and avenues for
future research.Comment: 14 pages, 2 figures. Written as a chapter for forthcoming Advances in
network clustering and blockmodeling, and based on an extended version of The
many facets of community detection in complex networks, Appl. Netw. Sci. 2: 4
(2017) by the same author
Markov dynamics as a zooming lens for multiscale community detection: non clique-like communities and the field-of-view limit
In recent years, there has been a surge of interest in community detection
algorithms for complex networks. A variety of computational heuristics, some
with a long history, have been proposed for the identification of communities
or, alternatively, of good graph partitions. In most cases, the algorithms
maximize a particular objective function, thereby finding the `right' split
into communities. Although a thorough comparison of algorithms is still
lacking, there has been an effort to design benchmarks, i.e., random graph
models with known community structure against which algorithms can be
evaluated. However, popular community detection methods and benchmarks normally
assume an implicit notion of community based on clique-like subgraphs, a form
of community structure that is not always characteristic of real networks.
Specifically, networks that emerge from geometric constraints can have natural
non clique-like substructures with large effective diameters, which can be
interpreted as long-range communities. In this work, we show that long-range
communities escape detection by popular methods, which are blinded by a
restricted `field-of-view' limit, an intrinsic upper scale on the communities
they can detect. The field-of-view limit means that long-range communities tend
to be overpartitioned. We show how by adopting a dynamical perspective towards
community detection (Delvenne et al. (2010) PNAS:107: 12755-12760; Lambiotte et
al. (2008) arXiv:0812.1770), in which the evolution of a Markov process on the
graph is used as a zooming lens over the structure of the network at all
scales, one can detect both clique- or non clique-like communities without
imposing an upper scale to the detection. Consequently, the performance of
algorithms on inherently low-diameter, clique-like benchmarks may not always be
indicative of equally good results in real networks with local, sparser
connectivity.Comment: 20 pages, 6 figure
Social Event Detection in Massive Mobile Phone Data Using Probabilistic Location Inference
Abstract—The unprecedented amount of data from mobile phones creates new possibilities to analyze various aspects of human behavior. Over the last few years, much effort has been devoted to studying the mobility patterns of humans. In this paper we will focus on unusually large gatherings of people, i.e. unusual social events. We introduce the methodology of detecting such social events in massive mobile phone data, based on a Bayesian location inference framework. More specifically, we also develop a framework for deciding who is attending an event. We demonstrate the method on a few examples. Finally, we discuss some possible future approaches for event detection, and some possible analyses of the detected social events. I
Different Approaches to Community Detection
A precise definition of what constitutes a community in networks has remained elusive. Consequently, network scientists have compared community detection algorithms on benchmark networks with a particular form of community structure and classified them based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different reasons for why we would want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different approaches to community detection also delineates the many lines of research and points out open directions and avenues for future research