95,290 research outputs found
Evaluating Overfit and Underfit in Models of Network Community Structure
A common data mining task on networks is community detection, which seeks an
unsupervised decomposition of a network into structural groups based on
statistical regularities in the network's connectivity. Although many methods
exist, the No Free Lunch theorem for community detection implies that each
makes some kind of tradeoff, and no algorithm can be optimal on all inputs.
Thus, different algorithms will over or underfit on different inputs, finding
more, fewer, or just different communities than is optimal, and evaluation
methods that use a metadata partition as a ground truth will produce misleading
conclusions about general accuracy. Here, we present a broad evaluation of over
and underfitting in community detection, comparing the behavior of 16
state-of-the-art community detection algorithms on a novel and structurally
diverse corpus of 406 real-world networks. We find that (i) algorithms vary
widely both in the number of communities they find and in their corresponding
composition, given the same input, (ii) algorithms can be clustered into
distinct high-level groups based on similarities of their outputs on real-world
networks, and (iii) these differences induce wide variation in accuracy on link
prediction and link description tasks. We introduce a new diagnostic for
evaluating overfitting and underfitting in practice, and use it to roughly
divide community detection methods into general and specialized learning
algorithms. Across methods and inputs, Bayesian techniques based on the
stochastic block model and a minimum description length approach to
regularization represent the best general learning approach, but can be
outperformed under specific circumstances. These results introduce both a
theoretically principled approach to evaluate over and underfitting in models
of network community structure and a realistic benchmark by which new methods
may be evaluated and compared.Comment: 22 pages, 13 figures, 3 table
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
An efficient and principled method for detecting communities in networks
A fundamental problem in the analysis of network data is the detection of
network communities, groups of densely interconnected nodes, which may be
overlapping or disjoint. Here we describe a method for finding overlapping
communities based on a principled statistical approach using generative network
models. We show how the method can be implemented using a fast, closed-form
expectation-maximization algorithm that allows us to analyze networks of
millions of nodes in reasonable running times. We test the method both on
real-world networks and on synthetic benchmarks and find that it gives results
competitive with previous methods. We also show that the same approach can be
used to extract nonoverlapping community divisions via a relaxation method, and
demonstrate that the algorithm is competitively fast and accurate for the
nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl
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