Using Wikipedia Anchor Text and Weighted Clustering Coefficient to Enhance the Traditional Multi-document Summarization

2012 ◽  
pp. 390-401 ◽  
Author(s):  
Niraj Kumar ◽  
Kannan Srinathan ◽  
Vasudeva Varma
Keyword(s):  
Clustering Coefficient ◽  
Document Summarization ◽  
Anchor Text ◽  
Weighted Clustering

Ranking weighted clustering coefficient in large dynamic graphs

2016 ◽  
Vol 20 (5) ◽  
pp. 855-883 ◽  
Author(s):  
Xuefei Li ◽  
Lijun Chang ◽  
Kai Zheng ◽  
Zi Huang ◽  
Xiaofang Zhou
Keyword(s):  
Clustering Coefficient ◽  
Dynamic Graphs ◽  
Weighted Clustering

scholarly journals Statistical Analysis of Weighted Networks

2008 ◽  
Vol 2008 ◽  
pp. 1-16 ◽  
Author(s):  
I. E. Antoniou ◽  
E. T. Tsompa
Keyword(s):  
Degree Distribution ◽  
Path Length ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Weighted Graphs ◽  
Statistical Characterization ◽  
Weighted Networks ◽  
Weighted Clustering ◽  
Statistical Regularities

The purpose of this paper is to assess the statistical characterization of weighted networks in terms of the generalization of the relevant parameters, namely, average path length, degree distribution, and clustering coefficient. Although the degree distribution and the average path length admit straightforward generalizations, for the clustering coefficient several different definitions have been proposed in the literature. We examined the different definitions and identified the similarities and differences between them. In order to elucidate the significance of different definitions of the weighted clustering coefficient, we studied their dependence on the weights of the connections. For this purpose, we introduce the relative perturbation norm of the weights as an index to assess the weight distribution. This study revealed new interesting statistical regularities in terms of the relative perturbation norm useful for the statistical characterization of weighted graphs.

scholarly journals Characterization of Cervical-Cranial Muscle Network in Correlation with Vocal Features

2021 ◽  
Author(s):  
Rory O’Keeffe ◽  
Seyed Yahya Shirazi ◽  
Sarmad Mehrdad ◽  
Tyler Crosby ◽  
Aaron M. Johnson ◽  
...  
Keyword(s):  
Effect Size ◽  
Objective Evaluation ◽  
Clustering Coefficient ◽  
Voice Disorder ◽  
Vocal Performance ◽  
High Effect Size ◽  
Weighted Clustering ◽  
High Effect ◽  
Cranial Muscle ◽  
First Time

AbstractObjective evaluation of physiological responses using non-invasive methods has attracted great interest regarding the assessment of vocal performance and disorders. This paper, for the first time, demonstrates that the topographical features of the cervical-cranial intermuscular coherence network generated using surface electromyography (sEMG) have a strong potential for detecting subtle changes in vocal performance. For this purpose, in this paper, 12 sEMG signals were collected from six cervical and cranial muscles bilaterally. Data were collected from four subjects without a history of a voice disorder performing a series of vocal tasks. The vocal tasks were varied phonation (an /a/ sustained for the maximal duration with combinations of two levels of loudness and two levels of pitch), a pitch glide from low to high, singing a familiar song, spontaneous speech, and reading with different loudness levels. The varied phonation tasks showed the median degree, and weighted clustering coefficient of the coherence-based intermuscular network ascends monotonically, with a high effect size (|rrb| = 0.52). The set of tasks, including pitch glide, singing, and speech, was significantly distinguishable using the network features as both degree and weighted clustering coefficient had a very high effect size (|rrb| > 0.83) across these tasks. Also, pitch glide has the highest degree and weighted clustering coefficient among all tasks (degree > 0.6, weighted clustering coefficient > 0.6). Spectrotemporal features performed far less effective than the proposed functional muscle network metrics to differentiate the vocal tasks. The highest effect size for spectrotemporal features was only |rrb| = 0.19. In this paper, for the first time, the power of a cervical-cranial muscle network has been demonstrated as a neurophysiological window to vocal performance. The results also shed light on the tasks with the highest network involvement, which may be potentially used in monitoring vocal disorders and tracking rehabilitation progress.

TRAPPING ON WEIGHTED TETRAHEDRON KOCH NETWORKS WITH SMALL-WORLD PROPERTY

Fractals ◽  
2014 ◽  
Vol 22 (01n02) ◽  
pp. 1450006 ◽  
Author(s):  
MEIFENG DAI ◽  
QI XIE ◽  
LIFENG XI
Keyword(s):  
Small World ◽  
Clustering Coefficient ◽  
Weight Factor ◽  
First Passage ◽  
Mean First Passage Times ◽  
Weighted Clustering ◽  
Self Similar ◽  
Small World Property ◽  
Analytical Expressions ◽  
Passage Times

In this paper, we present weighted tetrahedron Koch networks depending on a weight factor. According to their self-similar construction, we obtain the analytical expressions of the weighted clustering coefficient and average weighted shortest path (AWSP). The obtained solutions show that the weighted tetrahedron Koch networks exhibits small-world property. Then, we calculate the average receiving time (ART) on weighted-dependent walks, which is the sum of mean first-passage times (MFPTs) for all nodes absorpt at the trap located at a hub node. We find that the ART exhibits a sublinear or linear dependence on network order.

scholarly journals Efficient computation of the Weighted Clustering Coefficient

2016 ◽  
Vol 12 (6) ◽  
pp. 381-401
Author(s):  
Silvio Lattanzi ◽  
Stefano Leonardi
Keyword(s):  
Clustering Coefficient ◽  
Efficient Computation ◽  
Weighted Clustering
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