Attributed Network Embedding for Learning in a Dynamic Environment

Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance various learning tasks such as node classification, network clustering, and link prediction. Most, if not all, of the existing works, are overwhelmingly performed in the context of plain and static networks. Nonetheless, in reality, network structure often evolves over time with addition/deletion of links and nodes. Also, a vast majority of real-world networks are associated with a rich set of node attributes, and their attribute values are also naturally changing, with the emerging of new content patterns and the fading of old content patterns. These changing characteristics motivate us to seek an effective embedding representation to capture network and attribute evolving patterns, which is of fundamental importance for learning in a dynamic environment. To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly. In this paper, we tackle this problem by proposing a novel dynamic attributed network embedding framework - DANE. In particular, DANE first provides an offline method for a consensus embedding and then leverages matrix perturbation theory to maintain the freshness of the end embedding results in an online manner. We perform extensive experiments on both synthetic and real attributed networks to corroborate the effectiveness and efficiency of the proposed framework.Comment: 10 page

3-Chloro-N′-[(2-meth­oxy­naphthalen-1-yl)methyl­idene]benzohydrazide

The title compound, C19H15ClN2O2, was prepared by the reaction of 2-meth­oxy-1-naphthaldehyde with 3-chloro­benzohydrazide in methanol. The dihedral angle between the benzene ring and the naphthyl ring system is 69.0 (3)°. In the crystal, inter­molecular N—H⋯O hydrogen bonds link the mol­ecules into chains along the c axis. The crystal packing exhibits π–π inter­actions, as indicated by distances of 3.768 (3) Å between the centroids of the naphthyl rings of neighbouring mol­ecules

Cold Dark Matter Isocurvature Perturbations: Cosmological Constraints and Applications

In this paper we present the constraints on cold dark matter (CDM) isocurvature contributions to the cosmological perturbations. By employing Markov Chain Monte Carlo method (MCMC), we perform a global analysis for cosmological parameters using the latest astronomical data, such as 7-year Wilkinson Microwave Anisotropy Probe (WMAP7) observations, matter power spectrum from the Sloan Digital Sky Survey (SDSS) luminous red galaxies (LRG), and "Union2" type Ia Supernovae (SNIa) sample. We find that the correlated mixture of adiabatic and isocurvature modes are mildly better fitting to the current data than the pure adiabatic ones, with the minimal $\chi^2$ given by the likelihood analysis being reduced by 3.5. We also obtain a tight limit on the fraction of the CDM isocurvature contributions, which should be less than 14.6% at 95% confidence level. With the presence of the isocurvature modes, the adiabatic spectral index becomes slightly bigger, n_s^{\rm adi}=0.972\pm0.014~(1\,\sigma), and the tilt for isocurvature spectrum could be large, namely, the best fit value is n_s^{\rm iso}=3.020. Finally, we discuss the effect on WMAP normalization priors, shift parameter R, acoustic scale l_A and z_{*}, from the CDM isocurvaure perturbation. By fitting the mixed initial condition to the combined data, we find the mean values of R, l_A and z_{*} can be changed about 2.9\sigma, 2.8\sigma and 1.5\sigma respectively, comparing with those obtained in the pure adiabatic condition.Comment: 9 pages, 5 figures, 3 tables, references adde