6,866 research outputs found
Dynamic evolution of cross-correlations in the Chinese stock market
We study the dynamic evolution of cross-correlations in the Chinese stock
market mainly based on the random matrix theory (RMT). The correlation matrices
constructed from the return series of 367 A-share stocks traded on the Shanghai
Stock Exchange from January 4, 1999 to December 30, 2011 are calculated over a
moving window with a size of 400 days. The evolutions of the statistical
properties of the correlation coefficients, eigenvalues, and eigenvectors of
the correlation matrices are carefully analyzed. We find that the stock
correlations are significantly increased in the periods of two market crashes
in 2001 and 2008, during which only five eigenvalues significantly deviate from
the random correlation matrix, and the systemic risk is higher in these
volatile periods than calm periods. By investigating the significant
contributors of the deviating eigenvectors in different moving windows, we
observe a dynamic evolution behavior in business sectors such as IT,
electronics, and real estate, which lead the rise (drop) before (after) the
crashes
Modeling and Detecting Network Communities with the Fusion of Node Attributes
As a fundamental structure in real-world networks, communities can be
reflected by abundant node attributes with the fusion of graph topology. In
attribute-aware community detection, probabilistic generative models (PGMs)
have become the mainstream fusion method due to their principled
characterization and interpretation. Here, we propose a novel PGM without
imposing any distributional assumptions on attributes, which is superior to
existing PGMs that require attributes to be categorical or Gaussian
distributed. Based on the famous block model of graph structure, our model
fuses the attribute by describing its effect on node popularity using an
additional term. To characterize the effect quantitatively, we analyze the
detectability of communities for the proposed model and then establish the
requirements of the attribute-popularity term, which leads to a new scheme for
the model selection problem in attribute-aware community detection. With the
model determined, an efficient algorithm is developed to estimate the
parameters and to infer the communities. The proposed method is validated from
two aspects. First, the effectiveness of our algorithm is theoretically
guaranteed by the detectability condition, whose correctness is verified by
numerical experiments on artificial graphs. Second, extensive experiments show
that our method outperforms the competing approaches on a variety of real-world
networks.Comment: other authors do not want to preprin
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