Exploring the Characteristics of High-Speed Rail and Air Transportation Networks in China: A Weighted Network Approach

Qingyu Qi,Oh Kyoung Kwon 인하대학교 정석물류통상연구원 2021 JOURNAL OF INTERNATIONAL LOGISTICS AND TRADE Vol.19 No.2

This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

Weighted Local Naive Bayes Link Prediction

Wu, JieHua,Zhang, GuoJi,Ren, YaZhou,Zhang, XiaYan,Yang, Qiao Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.4

Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methods based on local structure are widely used to handle the predictive task. However, the results are still far from satisfied as major literatures neglect two important points: common neighbors produce different influence on potential links; weighted values associated with links in local structure are also different. In this paper, we adapt an effective link prediction model-local naive Bayes model into a weighted scenario to address this issue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link prediction framework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowing our model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensively be applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighted datasets. Experimental results show that our proposed approach performs better (by AUC and Prec) than several alternative methods for link prediction in weighted complex networks.

가중 네트워크를 위한 일반화된 지역중심성 지수

이재윤 한국정보관리학회 2015 정보관리학회지 Vol.32 No.2

네트워크 분석이 확산되면서 매개중심성이나 연결정도중심성과 같은 다양한 중심성 지수가 개발되어 활용되고 있으나, 가중 네트워크에서 지역중심성을 측정할 수 있는 지수로는 최근접이웃중심성 이외에는 거의 알려져 있지 않다. 이 연구에서는 가중 네트워크를 위한 일반화된 지역중심성 지수인 이웃중심성 지수를 새롭게 제안한다. 이웃중심성 지수는 파라미터 α를 사용하여 이진 네트워크를 위한 연결정도중심성 지수와 가중 네트워크를 위한 최근접이웃중심성 지수를 일반화한 것이다. 6가지 실제 네트워크 데이터를 대상으로 하여 제안된 지수의 특징과 적정 파라미터 값을 살펴보는 실험을 수행하고 결과를 보고하였다. While there are several measures for node centralities, such as betweenness and degree, few centrality measures for local centralities in weighted networks have been suggested. This study developed a generalized centrality measure for calculating local centralities in weighted networks. Neighbor centrality, which was suggested in this study, is the generalization of the degree centrality for binary networks and the nearest neighbor centrality for weighted networks with the parameter α. The characteristics of suggested measure and the proper value of parameter α are investigated with 6 real network datasets and the results are reported.

Weighted Local Naive Bayes Link Prediction

( Jiehua Wu ),( Guoji Zhang ),( Yazhou Ren ),( Xiayan Zhang ),( Qiao Yang ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.4

Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methods based on local structure are widely used to handle the predictive task. However, the results are still far from satisfied as major literatures neglect two important points: common neighbors produce different influence on potential links; weighted values associated with links in local structure are also different. In this paper, we adapt an effective link prediction model―local naive Bayes model into a weighted scenario to address this issue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link prediction framework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowing our model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensively be applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighted datasets. Experimental results show that our proposed approach performs better (by AUC and Prec) than several alternative methods for link prediction in weighted complex networks.

Privacy Protection Method for Sensitive Weighted Edges in Social Networks

( Weihua Gong ),( Rong Jin ),( Yanjun Li ),( Lianghuai Yang ),( Jianping Mei ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.2

Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.

On node criticality of the Northeast Asian air route network

Kim, Seyun,Yoon, Yoonjin Elsevier 2019 Journal of air transport management Vol.80 No.-

<P><B>Abstract</B></P> <P>In this paper, air route network robustness of the rapidly growing Northeast Asian region is assessed based on the node criticality. Air transport network is modeled and constructed as network of Air Route Segment (ARS), which is the minimum unit constituting air routes. Three variations of networks – unweighted, distance-weighted and demand-weighted Air Route Segment Networks (ARSN) are considered. Although not scale-free, the network is more vulnerable to targeted attacks than random failure, with a set of critical nodes identified as ‘pseudo-hubs’. When loss in flight operation is measured in the disrupted network, rerouting improved flight operability significantly. Findings on the set of critical nodes provide key insights for vulnerability of the network, especially in the context of regional coordination against various natural and manmade risks.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Northeast Asian air transport network is modeled as air route segment network. </LI> <LI> Some channeling nodes are identified as ‘pseudo-hubs’ of the regional airspace. </LI> <LI> Critical nodes in demand-weighted network are mostly located in Chinese airspace. </LI> <LI> Critical nodes in distance-weighted network are located in geographical boundary. </LI> <LI> Rerouting greatly improves the loss in flight operations in disrupted networks. </LI> </UL> </P>

Community Discovery in Weighted Networks Based on the Similarity of Common Neighbors

Miaomiao Liu,Jingfeng Guo,Jing Chen 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.5

In view of the deficiencies of existing weighted similarity indexes, a hierarchical clustering method initializeexpand-merge (IEM) is proposed based on the similarity of common neighbors for community discovery inweighted networks. Firstly, the similarity of the node pair is defined based on the attributes of their commonneighbors. Secondly, the most closely related nodes are fast clustered according to their similarity to form initialcommunities and expand the communities. Finally, communities are merged through maximizing themodularity so as to optimize division results. Experiments are carried out on many weighted networks, whichhave verified the effectiveness of the proposed algorithm. And results show that IEM is superior to weightedcommon neighbor (CN), weighted Adamic-Adar (AA) and weighted resources allocation (RA) when usingthe weighted modularity as evaluation index. Moreover, the proposed algorithm can achieve more reasonablecommunity division for weighted networks compared with cluster-recluster-merge-algorithm (CRMA)algorithm.

Community Discovery in Weighted Networks Based on the Similarity of Common Neighbors

Liu, Miaomiao,Guo, Jingfeng,Chen, Jing Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.5

In view of the deficiencies of existing weighted similarity indexes, a hierarchical clustering method initialize-expand-merge (IEM) is proposed based on the similarity of common neighbors for community discovery in weighted networks. Firstly, the similarity of the node pair is defined based on the attributes of their common neighbors. Secondly, the most closely related nodes are fast clustered according to their similarity to form initial communities and expand the communities. Finally, communities are merged through maximizing the modularity so as to optimize division results. Experiments are carried out on many weighted networks, which have verified the effectiveness of the proposed algorithm. And results show that IEM is superior to weighted common neighbor (CN), weighted Adamic-Adar (AA) and weighted resources allocation (RA) when using the weighted modularity as evaluation index. Moreover, the proposed algorithm can achieve more reasonable community division for weighted networks compared with cluster-recluster-merge-algorithm (CRMA) algorithm.

공동연구 네트워크 분석을 위한 중심성 지수에 대한 비교 연구

이재윤 한국정보관리학회 2014 정보관리학회지 Vol.31 No.3

This study explores the characteristics of centrality measures for analyzing researchers’ impact and structural positions in research collaboration networks. We investigate four binary network centrality measures (degree centrality, closeness centrality, betweenness centrality, and PageRank), and seven existing weighted network centrality measures (triangle betweenness centrality, mean association, weighted PageRank, collaboration h-index, collaboration hs-index, complex degree centrality, and c-index) for research collaboration networks. And we propose SSR, which is a new weighted centrality measure for collaboration networks. Using research collaboration data from three different research domains including architecture, library and information science, and marketing, the above twelve centrality measures are calculated and compared each other. Results indicate that the weighted network centrality measures are needed to consider collaboration strength as well as collaboration range in research collaboration networks. We also recommend that when considering both collaboration strength and range, it is appropriate to apply triangle betweenness centrality and SSR to investigate global centrality and local centrality in collaboration networks. 이 연구의 목적은 공동연구 네트워크에서 연구자의 영향력과 입지를 분석하는데 사용되는 중심성 지수들의 특징에 대해서 고찰하는 것이다. 전통적인 이진 네트워크 중심성 지수로는 연결정도중심성, 매개중심성, 근접중심성, 페이지랭크를 다루었고, 공동연구 네트워크에서의 중심성을 측정하기 위해서 개발되었거나 사용된 가중 네트워크 중심성 지수로는 삼각매개중심성, 평균연관성, 가중페이지랭크, 공동연구 h-지수와 공동연구 hs-지수, 복합연결정도중심성, c-지수에 대해서 살펴보았으며, 새로운 지수로 제곱근합 지수 SSR을 제안하였다. 이들 12종의 중심성 지수를 건축학, 문헌정보학, 마케팅 분야의 세 가지 공동연구 네트워크에 적용해본 결과 각 지수들의 특성과 지수 간 관계를 파악할 수 있었다. 분석 결과 공동연구 네트워크에서 공동연구 범위와 공동연구 강도를 모두 고려하기 위해서는 가중 네트워크 중심성 지수를 사용해야 하는 것으로 나타났다. 특히 공동연구 범위와 강도를 모두 고려하는 전역중심성을 측정하기 위해서는 삼각매개중심성 지수를 사용하고, 지역중심성을 측정하기 위해서는 SSR 지수를 사용하는 것이 바람직하다고 제안하였다.

Dynamic Entropy Based Combination Weighted Clustering Approach for High-Speed Ad hoc Network

Jianli Xie,Cuiran Li,Hui Zhou 보안공학연구지원센터 2015 International Journal of Future Generation Communi Vol.8 No.3

Weight based clustering has become the mainstream clustering algorithm in low-speed Ad hoc networks because of its excellent cluster stability. However, due to the dynamic topology changing in high-speed Ad hoc network, the cluster stability (network stability) decreased and the cluster maintenance costs increased sharply. To solve the problem, we propose a dynamic entropy based combination weighted clustering approach (DECW). First, according to the history messages of an evaluation node in the network, the upper bound and the lower bound value of each clustering index will be recorded, so the information entropy deviation of the indexes and dynamic entropy weight of each node can be obtained. After, the linear combination weights set of evaluation nodes is modeled as the second-order norm game , and the weight vector deviation is minimized as the optimization goal to get the multi-node dynamic entropy weights. In the cluster maintenance, a new Monte Carlo optimization is proposed to avoid the frequent cluster-heads (CHs) replacement induced of high node mobility of. Simulation results reveal that the proposed approach has the better adaptability in high-speed mobile environment.