Exploratory factor analysis of graphical features for link prediction in social networks

Social Networks attract much attention due to their ability to replicate social interactions at scale. Link prediction, or the assessment of which unconnected nodes are likely to connect in the future, is an interesting but non-trivial research area. Three approaches exist to deal with the link prediction problem: feature-based models, Bayesian probabilistic models, probabilistic relational models. In feature-based methods, graphical features are extracted and used for classification. Usually, these features are subdivided into three feature groups based on their formula. Some formulas are extracted based on neighborhood graph traverse. Accordingly, there exists three groups of features, neighborhood features, path-based features, node-based features. In this paper, we attempt to validate the underlying structure of topological features used in feature-based link prediction. The results of our analysis indicate differing results from the prevailing grouping of these features, which indicates that current literatures\u27 classification of feature groups should be redefined. Thus, the contribution of this work is exploring the factor loading of graphical features in link prediction in social networks. To the best of our knowledge, there is no prior studies had addressed it

Histological image classification using biologically interpretable shape-based features

© 2013 Kothari et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.DOI: 10.1186/1471-2342-13-9Background: Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. Methods: We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. Results: The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Conclusions: Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions

Effect of Nozzle Port Angle on Transient Flow and Surface Slag Behavior During Continuous Steel-Slab Casting

Undesirable flow variations can cause severe instabilities at the interface between liquid mold flux and molten steel across the mold top-region during continuous steel casting, resulting in surface defects in the final products. A three-dimensional Large Eddy Simulation (LES) model using the volume of fluid method for the slag and molten steel phases is validated with plant measurements, and applied to gain new insights into the effects of nozzle port angle on transient flow, top slag/steel interface movement, and slag behavior during continuous slab casting under nominally steady conditions. Upward-angled ports produce a single-roll flow pattern with lower surface velocity, due to rapid momentum dissipation of the spreading jet. However, strong jet wobbling from the port leads to greater interface variations. Severe level drops allow easy entrapment of liquid flux by the solidifying steel shell at the meniscus. Sudden level rises may also be detrimental, leading to overflow of the solidified meniscus region. Downward-angled ports produce a classic double-roll pattern with less jet turbulence and a more stable interface everywhere except near the narrow faces. Finally, the flow patterns, surface velocity, and level predicted from the validated LES model are compared with steady-state standard k-epsilon model predictions.11Nsciescopu