Community Structure Detection in Complex Networks with Partial Background Information

Constrained clustering has been well-studied in the unsupervised learning society. However, how to encode constraints into community structure detection, within complex networks, remains a challenging problem. In this paper, we propose a semi-supervised learning framework for community structure detection. This framework implicitly encodes the must-link and cannot-link constraints by modifying the adjacency matrix of network, which can also be regarded as de-noising the consensus matrix of community structures. Our proposed method gives consideration to both the topology and the functions (background information) of complex network, which enhances the interpretability of the results. The comparisons performed on both the synthetic benchmarks and the real-world networks show that the proposed framework can significantly improve the community detection performance with few constraints, which makes it an attractive methodology in the analysis of complex networks

Modelling the number counts of early-type galaxies by pure luminosity evolution

In this paper, we explore the plausible luminosity evolution of early-type galaxies in different cosmological models by constructing a set of pure luminosity evolution (PLE) models via the choices of the star formation rate (SFR) parameters and formation redshift $z_f$ of galaxies, with the observational constraints derived from the Hubble Space Telescope (HST) morphological number counts for elliptical and S0 galaxies of the Medium Deep Survey (MDS) and the Hubble Deep Field (HDF). We find that the number counts of early-type galaxies can be explained by the pure luminosity evolution models, without invoking exotic scenarios such as merging or introducing an additional population. But the evolution should be nearly passive, with a high $z_f$ assumed. The conclusion is valid in all of the three cosmological models we adopted in this paper. We also present the redshift distributions for three bins of observed magnitudes in F814w pass-band, to show at which redshift are the objects that dominate the counts at a given magnitude. The predictions of the redshift distribution of $22.5<b_j<24.0$ are also presented for comparison with future data.Comment: Plain tex, 15pages, 9 eps figures, plus an extra figure fig2c.eps, with the tex-macro mn.tex. MNRAS, accepte