13,074 research outputs found
An Ensemble Framework for Detecting Community Changes in Dynamic Networks
Dynamic networks, especially those representing social networks, undergo
constant evolution of their community structure over time. Nodes can migrate
between different communities, communities can split into multiple new
communities, communities can merge together, etc. In order to represent dynamic
networks with evolving communities it is essential to use a dynamic model
rather than a static one. Here we use a dynamic stochastic block model where
the underlying block model is different at different times. In order to
represent the structural changes expressed by this dynamic model the network
will be split into discrete time segments and a clustering algorithm will
assign block memberships for each segment. In this paper we show that using an
ensemble of clustering assignments accommodates for the variance in scalable
clustering algorithms and produces superior results in terms of
pairwise-precision and pairwise-recall. We also demonstrate that the dynamic
clustering produced by the ensemble can be visualized as a flowchart which
encapsulates the community evolution succinctly.Comment: 6 pages, under submission to HPEC Graph Challeng
Hierarchical growing cell structures: TreeGCS
We propose a hierarchical clustering algorithm (TreeGCS) based upon the Growing Cell Structure (GCS) neural network of Fritzke. Our algorithm refines and builds upon the GCS base, overcoming an inconsistency in the original GCS algorithm, where the network topology is susceptible to the ordering of the input vectors. Our algorithm is unsupervised, flexible, and dynamic and we have imposed no additional parameters on the underlying GCS algorithm. Our ultimate aim is a hierarchical clustering neural network that is both consistent and stable and identifies the innate hierarchical structure present in vector-based data. We demonstrate improved stability of the GCS foundation and evaluate our algorithm against the hierarchy generated by an ascendant hierarchical clustering dendogram. Our approach emulates the hierarchical clustering of the dendogram. It demonstrates the importance of the parameter settings for GCS and how they affect the stability of the clustering
Cross-correlation Weak Lensing of SDSS Galaxy Clusters I: Measurements
This is the first in a series of papers on the weak lensing effect caused by
clusters of galaxies in Sloan Digital Sky Survey. The photometrically selected
cluster sample, known as MaxBCG, includes ~130,000 objects between redshift 0.1
and 0.3, ranging in size from small groups to massive clusters. We split the
clusters into bins of richness and luminosity and stack the surface density
contrast to produce mean radial profiles. The mean profiles are detected over a
range of scales, from the inner halo (25 kpc/h) well into the surrounding large
scale structure (30 Mpc/h), with a significance of 15 to 20 in each bin. The
signal over this large range of scales is best interpreted in terms of the
cluster-mass cross-correlation function. We pay careful attention to sources of
systematic error, correcting for them where possible. The resulting signals are
calibrated to the ~10% level, with the dominant remaining uncertainty being the
redshift distribution of the background sources. We find that the profiles
scale strongly with richness and luminosity. We find the signal within a given
richness bin depends upon luminosity, suggesting that luminosity is more
closely correlated with mass than galaxy counts. We split the samples by
redshift but detect no significant evolution. The profiles are not well
described by power laws. In a subsequent series of papers we invert the
profiles to three-dimensional mass profiles, show that they are well fit by a
halo model description, measure mass-to-light ratios and provide a cosmological
interpretation.Comment: Paper I in a series; v2.0 includes ApJ referee's suggestion
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