1,564 research outputs found
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
Modularity functions maximization with nonnegative relaxation facilitates community detection in networks
We show here that the problem of maximizing a family of quantitative
functions, encompassing both the modularity (Q-measure) and modularity density
(D-measure), for community detection can be uniformly understood as a
combinatoric optimization involving the trace of a matrix called modularity
Laplacian. Instead of using traditional spectral relaxation, we apply
additional nonnegative constraint into this graph clustering problem and design
efficient algorithms to optimize the new objective. With the explicit
nonnegative constraint, our solutions are very close to the ideal community
indicator matrix and can directly assign nodes into communities. The
near-orthogonal columns of the solution can be reformulated as the posterior
probability of corresponding node belonging to each community. Therefore, the
proposed method can be exploited to identify the fuzzy or overlapping
communities and thus facilitates the understanding of the intrinsic structure
of networks. Experimental results show that our new algorithm consistently,
sometimes significantly, outperforms the traditional spectral relaxation
approaches
Neighborhood Overlapped Propagation Algorithm For Community Detection Based On Label Time-Sequence
The community detection algorithms based on label propagation (LPA) receive broad attention for the advantages of near-linear complexity and no prerequisite for any object function or cluster number. However, the propagation of labels contains uncertainty and randomness, which affects the accuracy and stability of the LPA algorithm. In this study, we propose an efficient detection method based on COPRA with Time-sequence (COPRA_TS). Firstly, the labels are sorted according to a new label importance measure. Then, the label of each vertex is updated according to time-sequence topology measure. The experiments on both the artificial datasets and the real-world datasets demonstrate that the quality of communities discovered by COPRA_TS algorithm is improved with a better stability. At last some future research topics are given
Community Detection in Quantum Complex Networks
Determining community structure is a central topic in the study of complex
networks, be it technological, social, biological or chemical, in static or
interacting systems. In this paper, we extend the concept of community
detection from classical to quantum systems---a crucial missing component of a
theory of complex networks based on quantum mechanics. We demonstrate that
certain quantum mechanical effects cannot be captured using current classical
complex network tools and provide new methods that overcome these problems. Our
approaches are based on defining closeness measures between nodes, and then
maximizing modularity with hierarchical clustering. Our closeness functions are
based on quantum transport probability and state fidelity, two important
quantities in quantum information theory. To illustrate the effectiveness of
our approach in detecting community structure in quantum systems, we provide
several examples, including a naturally occurring light-harvesting complex,
LHCII. The prediction of our simplest algorithm, semiclassical in nature,
mostly agrees with a proposed partitioning for the LHCII found in quantum
chemistry literature, whereas our fully quantum treatment of the problem
uncovers a new, consistent, and appropriately quantum community structure.Comment: 16 pages, 4 figures, 1 tabl
Leveraging Node Attributes for Incomplete Relational Data
Relational data are usually highly incomplete in practice, which inspires us
to leverage side information to improve the performance of community detection
and link prediction. This paper presents a Bayesian probabilistic approach that
incorporates various kinds of node attributes encoded in binary form in
relational models with Poisson likelihood. Our method works flexibly with both
directed and undirected relational networks. The inference can be done by
efficient Gibbs sampling which leverages sparsity of both networks and node
attributes. Extensive experiments show that our models achieve the
state-of-the-art link prediction results, especially with highly incomplete
relational data.Comment: Appearing in ICML 201
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