scholarly journals Weighted Complex Network Analysis of Pakistan Highways

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Yasir Tariq Mohmand ◽  
Aihu Wang
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Public Transportation ◽  
Road Traffic ◽  
Practical Importance ◽  
Small World ◽  
Complex Network Analysis ◽  
The Road ◽  
Highway Network ◽  
Weighted Complex Network

The structure and properties of public transportation networks have great implications in urban planning, public policies, and infectious disease control. This study contributes a weighted complex network analysis of travel routes on the national highway network of Pakistan. The network is responsible for handling 75 percent of the road traffic yet is largely inadequate, poor, and unreliable. The highway network displays small world properties and is assortative in nature. Based on the betweenness centrality of the nodes, the most important cities are identified as this could help in identifying the potential congestion points in the network. Keeping in view the strategic location of Pakistan, such a study is of practical importance and could provide opportunities for policy makers to improve the performance of the highway network.

scholarly journals A Study on Differences between Simplified and Traditional Chinese Based on Complex Network Analysis of the Word Co-Occurrence Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Zhongqiang Jiang ◽  
Dongmei Zhao ◽  
Jiangbin Zheng ◽  
Yidong Chen
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Small World ◽  
Motif Analysis ◽  
Scale Free ◽  
Complex Network Analysis ◽  
Simplified Chinese ◽  
Language Analysis ◽  
Corpus Size ◽  
Global Understanding

Currently, most work on comparing differences between simplified and traditional Chinese only focuses on the character or lexical level, without taking the global differences into consideration. In order to solve this problem, this paper proposes to use complex network analysis of word co-occurrence networks, which have been successfully applied to the language analysis research and can tackle global characters and explore the differences between simplified and traditional Chinese. Specially, we first constructed a word co-occurrence network for simplified and traditional Chinese using selected news corpora. Then, the complex network analysis methods were performed, including network statistics analysis, kernel lexicon comparison, and motif analysis, to gain a global understanding of these networks. After that, the networks were compared based on the properties obtained. Through comparison, we can obtain three interesting results: first, the co-occurrence networks of simplified Chinese and traditional Chinese are both small-world and scale-free networks. However, given the same corpus size, the co-occurrence networks of traditional Chinese tend to have more nodes, which may be due to a large number of one-to-many character/word mappings from simplified Chinese to traditional Chinese; second, since traditional Chinese retains more ancient Chinese words and uses fewer weak verbs, the traditional Chinese kernel lexicons have more entries than the simplified Chinese kernel lexicons; third, motif analysis shows that there is no difference between the simplified Chinese network and the corresponding traditional Chinese network, which means that simplified and traditional Chinese are semantically consistent.

scholarly journals Complex Network Analysis of Pakistan Railways

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Yasir Tariq Mohmand ◽  
Aihu Wang
Keyword(s):  
Network Analysis ◽  
Structural Properties ◽  
Complex Network ◽  
Small World ◽  
Railway Network ◽  
Railway Stations ◽  
Complex Network Analysis

We study the structural properties of Pakistan railway network (PRN), where railway stations are considered as nodes while edges are represented by trains directly linking two stations. The network displays small world properties and is assortative in nature. Based on betweenness and closeness centralities of the nodes, the most important cities are identified with respect to connectivity as this could help in identifying the potential congestion points in the network.

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