scholarly journals Optimal Transport on Weighted Networks for Different Node Delivery Capability Schemes

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Fei Shao
Keyword(s):  
Real World ◽  
Optimal Transport ◽  
Node Degree ◽  
Weighted Network ◽  
Weighted Networks ◽  
Traffic Capacity

Many real networks can be best described by weighted networks with a diversity of interactions between nodes measured by the weights of the edges. It is of great importance to improve the overall capacity of these real-world networks. In this paper, the traffic capacity of weighted network is investigated based on three different node delivery capability schemes: the delivery capacity of each node is constant in the first scheme while in the second and third schemes it is proportional to its node degree and node strength. It is shown by simulations that the network transfer capacity depends strongly on the tunable parameter. And different tunable parameter is suitable for different node delivery capability.

scholarly journals Characterizing dissimilarity of weighted networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuanxiang Jiang ◽  
Meng Li ◽  
Ying Fan ◽  
Zengru Di
Keyword(s):  
Complex Network ◽  
Network Structure ◽  
Real World ◽  
Basic Problem ◽  
Experimental Results ◽  
Weighted Network ◽  
Weighted Networks ◽  
Comprehensive Information ◽  
Complementary Graph

AbstractMeasuring the dissimilarities between networks is a basic problem and wildly used in many fields. Based on method of the D-measure which is suggested for unweighted networks, we propose a quantitative dissimilarity metric of weighted network (WD-metric). Crucially, we construct a distance probability matrix of weighted network, which can capture the comprehensive information of weighted network. Moreover, we define the complementary graph and alpha centrality of weighted network. Correspondingly, several synthetic and real-world networks are used to verify the effectiveness of the WD-metric. Experimental results show that WD-metric can effectively capture the influence of weight on the network structure and quantitatively measure the dissimilarity of weighted networks. It can also be used as a criterion for backbone extraction algorithms of complex network.

Analyses of the response of a complex weighted network to nodes removal strategies considering links weight: The case of the Beijing urban road system

2018 ◽  
Vol 32 (05) ◽  
pp. 1850067 ◽  
Author(s):  
Michele Bellingeri ◽  
Zhe-Ming Lu ◽  
Davide Cassi ◽  
Francesco Scotognella
Keyword(s):  
Complex Network ◽  
Real World ◽  
Road Traffic ◽  
Network Efficiency ◽  
Weighted Network ◽  
Node Importance ◽  
Connected Cluster ◽  
Binary Network ◽  
Binary Networks ◽  
Weighted Rank

Complex network response to node loss is a central question in different fields of science ranging from physics, sociology, biology to ecology. Previous studies considered binary networks where the weight of the links is not accounted for. However, in real-world networks the weights of connections can be widely different. Here, we analyzed the response of real-world road traffic complex network of Beijing, the most prosperous city in China. We produced nodes removal attack simulations using classic binary node features and we introduced weighted ranks for node importance. We measured the network functioning during nodes removal with three different parameters: the size of the largest connected cluster (LCC), the binary network efficiency (Bin EFF) and the weighted network efficiency (Weg EFF). We find that removing nodes according to weighted rank, i.e. considering the weight of the links as a number of taxi flows along the roads, produced in general the highest damage in the system. Our results show that: (i) in order to model Beijing road complex networks response to nodes (intersections) failure, it is necessary to consider the weight of the links; (ii) to discover the best attack strategy, it is important to use nodes rank accounting links weight.

Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs

2019 ◽  
pp. 1183-1198
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Random Network ◽  
Maximal Clique ◽  
Closeness Centrality ◽  
Node Degree ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Scale Free ◽  
Centrality Metrics ◽  
Free Network

The authors present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. They consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC), and betweenness centrality (BWC). They define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. The authors observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. They observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).

Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs

2018 ◽  
pp. 6507-6521
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Random Network ◽  
Maximal Clique ◽  
Closeness Centrality ◽  
Node Degree ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Scale Free ◽  
Centrality Metrics ◽  
Free Network

We present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. We consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC) and betweenness centrality (BWC). We define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. We observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. We observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).

scholarly journals A Binary Search Algorithm for Correlation Study of Decay Centrality vs. Degree Centrality and Closeness Centrality

2017 ◽  
Vol 10 (2) ◽  
pp. 52
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Search Algorithm ◽  
Threshold Value ◽  
Search Space ◽  
Correlation Study ◽  
Binary Search ◽  
Closeness Centrality ◽  
Node Degree ◽  
Degree Centrality ◽  
Binary Search Algorithm

Results of correlation study (using Pearson's correlation coefficient, PCC) between decay centrality (DEC) vs. degree centrality (DEG) and closeness centrality (CLC) for a suite of 48 real-world networks indicate an interesting trend: PCC(DEC, DEG) decreases with increase in the decay parameter δ (0 < δ < 1) and PCC(DEC, CLC) decreases with decrease in δ. We make use of this trend of monotonic decrease in the PCC values (from both sides of the δ-search space) and propose a binary search algorithm that (given a threshold value r for the PCC) could be used to identify a value of δ (if one exists, we say there exists a positive δ-spacer) for a real-world network such that PCC(DEC, DEG) ≥ r as well as PCC(DEC, CLC) ≥ r. We show the use of the binary search algorithm to find the maximum Threshold PCC value rmax (such that δ-spacermax is positive) for a real-world network. We observe a very strong correlation between rmax and PCC(DEG, CLC) as well as observe real-world networks with a larger variation in node degree to more likely have a lower rmax value and vice-versa.

Efficient weighting strategy for enhancing synchronizability of complex networks

2018 ◽  
Vol 32 (11) ◽  
pp. 1850128 ◽  
Author(s):  
Youquan Wang ◽  
Feng Yu ◽  
Shucheng Huang ◽  
Juanjuan Tu ◽  
Yan Chen
Keyword(s):  
Complex Networks ◽  
Structural Properties ◽  
Real World ◽  
Centrality Measures ◽  
Node Degree ◽  
Scale Free ◽  
High Propensity ◽  
Network Link ◽  
Weighting Strategy ◽  
Weighting Methods

Networks with high propensity to synchronization are desired in many applications ranging from biology to engineering. In general, there are two ways to enhance the synchronizability of a network: link rewiring and/or link weighting. In this paper, we propose a new link weighting strategy based on the concept of the neighborhood subgroup. The neighborhood subgroup of a node i through node j in a network, i.e. [Formula: see text], means that node u belongs to [Formula: see text] if node u belongs to the first-order neighbors of j (not include i). Our proposed weighting schema used the local and global structural properties of the networks such as the node degree, betweenness centrality and closeness centrality measures. We applied the method on scale-free and Watts–Strogatz networks of different structural properties and show the good performance of the proposed weighting scheme. Furthermore, as model networks cannot capture all essential features of real-world complex networks, we considered a number of undirected and unweighted real-world networks. To the best of our knowledge, the proposed weighting strategy outperformed the previously published weighting methods by enhancing the synchronizability of these real-world networks.

scholarly journals GOSSIP ON WEIGHTED NETWORKS

2012 ◽  
Vol 15 (supp01) ◽  
pp. 1250061 ◽  
Author(s):  
MURSEL TASGIN ◽  
HALUK O. BINGOL
Keyword(s):  
Complex Networks ◽  
Weight Distribution ◽  
Human Interaction ◽  
Interaction Networks ◽  
Weighted Network ◽  
Weighted Networks ◽  
Human Interactions ◽  
Spreading Model ◽  
Good Parameter ◽  
Weighted Complex Networks

In this work, we analyze gossip spreading on weighted networks. We try to define a new metric to classify weighted complex networks using our model. The model proposed here is based on the gossip spreading model introduced by Lind et al. on unweighted networks. The new metric is based on gossip spreading activity in the network, which is correlated with both topology and relative edge weights in the network. The model gives more insight about the weight distribution and correlation of topology with edge weights in a network. It also measures how suitable a weighted network is for gossip spreading. We analyze gossip spreading on real weighted networks of human interactions. Six co-occurrence and seven social pattern networks are investigated. Gossip propagation is found to be a good parameter to distinguish co-occurrence and social pattern networks. As a comparison some miscellaneous networks of comparable sizes and computer generated networks based on ER, BA and WS models are also investigated. They are found to be quite different from the human interaction networks.

scholarly journals Oversampling for Imbalanced Data via Optimal Transport

2019 ◽  
Vol 33 ◽  
pp. 5605-5612 ◽  
Author(s):  
Yuguang Yan ◽  
Mingkui Tan ◽  
Yanwu Xu ◽  
Jiezhang Cao ◽  
Michael Ng ◽  
...  
Keyword(s):  
Real World ◽  
Optimal Transport ◽  
Imbalanced Data ◽  
Data Sets ◽  
Similar Distribution ◽  
Real World Data ◽  
Geometric Information ◽  
Minority Class ◽  
Real World Applications ◽  
Multiple Metrics

The issue of data imbalance occurs in many real-world applications especially in medical diagnosis, where normal cases are usually much more than the abnormal cases. To alleviate this issue, one of the most important approaches is the oversampling method, which seeks to synthesize minority class samples to balance the numbers of different classes. However, existing methods barely consider global geometric information involved in the distribution of minority class samples, and thus may incur distribution mismatching between real and synthetic samples. In this paper, relying on optimal transport (Villani 2008), we propose an oversampling method by exploiting global geometric information of data to make synthetic samples follow a similar distribution to that of minority class samples. Moreover, we introduce a novel regularization based on synthetic samples and shift the distribution of minority class samples according to loss information. Experiments on toy and real-world data sets demonstrate the efficacy of our proposed method in terms of multiple metrics.

Traffic dynamics on two-layer networks with community structure

2021 ◽  
pp. 2150272
Author(s):  
Jinlong Ma ◽  
Yi Zhou ◽  
Weiheng Wang ◽  
Yongqiang Zhang ◽  
Ruimei Zhao ◽  
...  
Keyword(s):  
Community Structure ◽  
Network Model ◽  
Real World ◽  
Traffic Congestion ◽  
Network Models ◽  
Community Networks ◽  
Traffic Dynamics ◽  
Community Strength ◽  
Traffic Capacity ◽  
Simulation Results

In terms of reducing traffic congestion, it should be understood that traffic dynamics depend on network structure. Most of complex networks in the real world can be represented by multi-layer and community structures, that is, the connections within the community are relatively close, and the connections between the community are relatively sparse. There are generally strong and weak community networks in community networks. In this work, the strong and weak community networks are used to construct two-layer network models of different scales, and then the influence of community structure on traffic capacity is analyzed. The simulation results show that when the two-layer network model is composed of two strong community networks, the traffic capacity is the largest, followed by the two-layer network model composed of two weak community networks, the traffic capacity is also relatively large. When the two subnetworks are of different community strength, the traffic capacity is relatively small.

Optimal Transport in Weighted Networks by Deleting Edges

2016 ◽  
Vol 9 (11) ◽  
pp. 397-406
Author(s):  
Shoubao Su ◽  
Mingjuan Xu ◽  
Yu Su ◽  
Fei Shao
Keyword(s):  
Optimal Transport ◽  
Weighted Networks
Sign in / Sign up

Export Citation Format

Share Document