Sentiment Classification for Online Comments Based on Random Network Theory

2010 ◽  
Vol 36 (6) ◽  
pp. 837-844 ◽  
Author(s):  
Feng YANG ◽  
Qin-Ke PENG ◽  
Tao XU
1995 ◽  
Vol 192-193 ◽  
pp. 92-97 ◽  
Author(s):  
Adrian C. Wright ◽  
Natalia M. Vedishcheva ◽  
Boris A. Shakhmatkin

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Joffrey Planckaert ◽  
Stamatios C. Nicolis ◽  
Jean-Louis Deneubourg ◽  
Cédric Sueur ◽  
Olivier Bles

Abstract Intranidal food dissemination through trophallactic exchanges is a fundamental issue in social insect colonies but its underlying mechanisms are far from being clear. In light of the division of work, network theory and collective food management we develop a framework to investigate the spatiotemporal dynamics of the trophallactic network in starved Lasius niger ant colonies. Thanks to tracking methods we are able to record spatial locations of the trophallactic interactions in the nest. We highlight quantitative differences between the foragers and non-foragers concerning their contributions, their roles (donor/recipient) and their spatial distributions. Moreover, at the intracaste level, we show interindividual differences in all activities and we characterise their nature. In particular, within each caste, all the individuals have the same probability to start their food exchange activity but their probability to exchange differs after their first trophallactic event. Interestingly, despite the highlighted interindividual differences, the trophallactic network does not differ from a random network.


2018 ◽  
Vol 52 (1 (245)) ◽  
pp. 34-40
Author(s):  
A.G. Kocharyan

In the paper the result of a research done by using the automated system xRandNet is presented, which is designed and implemented for generating and analyzing the main topological properties of some hierarchical models of random networks. The research is related to the connected component distribution of random block-hierarchical networks, which are quite new objects in the random network theory.


2018 ◽  
Author(s):  
Matthieu Gilson ◽  
Nikos E. Kouvaris ◽  
Gustavo Deco ◽  
Jean-François Mangin ◽  
Cyril Poupon ◽  
...  

AbstractNeuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often “static” despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time (in which input properties play a role), moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.


2018 ◽  
Vol 20 (21) ◽  
pp. 14725-14739 ◽  
Author(s):  
Projesh Kumar Roy ◽  
Markus Heyde ◽  
Andreas Heuer

The recent experimental discovery of a semi two-dimensional silica glass has offered a realistic description of the random network theory of a silica glass structure, initially discussed by Zachariasen.


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