scholarly journals Complex interaction networks of cytokines after transarterial chemotherapy in patients with hepatocellular carcinoma

2019 ◽  
Author(s):  
Dong Wook Jekarl ◽  
Seungok Lee ◽  
Jung Hyun Kwon ◽  
Soon Woo Nam ◽  
Jeong Won Jang ◽  
...  
Keyword(s):  
Adverse Outcome ◽  
Temporal Changes ◽  
Clustering Coefficient ◽  
Scale Free Network ◽  
Serum Samples ◽  
Cytokine Network ◽  
Scale Free ◽  
Degree Correlation ◽  
Free Network ◽  
Assortative Network

AbstractInflammation in the tumor microenvironment influences all stages of HCC development and progression as well as the anti-cancer response by immune system. In this study, we studied cytokine networks before and after transarterial chemotherapy (TACE). Serum samples obtained from 203 HCC patients treated with TACE were analyzed for inflammatory cytokines including interleukin (IL)-1β, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12, IL-13, IL-17, IL-22, TNF-α, IFN-γ, and C-reactive protein (CRP) levels. Cytokine concentrations were measured at day 0 (D0, baseline), day3 (D3), day7 (D7), and day 60 (D60) after TACE. Network analysis revealed that modules within cytokine network at D0 were lost by D60 and modularity value (Mc) was decreased from 0.177 at D0 to −0.091 at D60. D60 had the lowest network heterogeneity and lower diameter, clustering coefficient, network density and recruited nodes. Degree correlation revealed that assortative network turned to disassortative network by D60 indicating that the network gained scale free feature. CRP, IL-2 were components of modules related with adverse outcome and IL-13, favorable outcome. Median survival month of patient group with high and low values with P-values were as follows: D0 CRP, 9.5 month (M), 54.2M (P<0.0001); D0 IL-2, 39.9M, 56.1M (P=0.0084); D3 CRP, 31.3M, 55.1 M (P=0.0056); D7 CRP, 28.7M, 50.7M (P=0.0065); IL-13, 51.9M, 33.6M (P=0.06). Network modularity decreased with temporal changes. Components of modules that included CRP, IL-2 and IL-6 were associated with adverse outcome and short overall survival. These modules were dissolved by D60 after TACE. Degree correlation decreased by D60, indicating that the cytokine network gained the scale free network property as in other biological network. TACE treatment converted cytokine network from that with inflammatory module to that with scale free network feature and without modules. Further studies are required to verify temporal changes of cytokine network in HCC patients after TACE.

scholarly journals The Fractional Preferential Attachment Scale-Free Network Model

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 509
Author(s):  
Rafał Rak ◽  
Ewa Rak
Keyword(s):  
Network Model ◽  
Real World ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Clustering Coefficient ◽  
Node Degree ◽  
Scale Free Network ◽  
Scale Free ◽  
Degree Correlation ◽  
Free Network

Many networks generated by nature have two generic properties: they are formed in the process of preferential attachment and they are scale-free. Considering these features, by interfering with mechanism of the preferential attachment, we propose a generalisation of the Barabási–Albert model—the ’Fractional Preferential Attachment’ (FPA) scale-free network model—that generates networks with time-independent degree distributions p ( k ) ∼ k − γ with degree exponent 2 < γ ≤ 3 (where γ = 3 corresponds to the typical value of the BA model). In the FPA model, the element controlling the network properties is the f parameter, where f ∈ ( 0 , 1 ⟩ . Depending on the different values of f parameter, we study the statistical properties of the numerically generated networks. We investigate the topological properties of FPA networks such as degree distribution, degree correlation (network assortativity), clustering coefficient, average node degree, network diameter, average shortest path length and features of fractality. We compare the obtained values with the results for various synthetic and real-world networks. It is found that, depending on f, the FPA model generates networks with parameters similar to the real-world networks. Furthermore, it is shown that f parameter has a significant impact on, among others, degree distribution and degree correlation of generated networks. Therefore, the FPA scale-free network model can be an interesting alternative to existing network models. In addition, it turns out that, regardless of the value of f, FPA networks are not fractal.

scholarly journals The Analysis of the Properties of Bus Network Topology in Beijing Basing on Complex Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Hui Zhang ◽  
Peng Zhao ◽  
Jian Gao ◽  
Xiang-ming Yao
Keyword(s):  
Network Topology ◽  
Second Phase ◽  
Scale Free Network ◽  
Approach Space ◽  
Scale Free ◽  
Transport Dynamics ◽  
Big City ◽  
Free Network ◽  
Bus Network ◽  
Assortative Network

The transport network structure plays a crucial role in transport dynamics. To better understand the property of the bus network in big city and reasonably configure the bus lines and transfers, this paper seeks to take the bus network of Beijing as an example and mainly use space L and space P to analyze the network topology properties. The approach is applied to all the bus lines in Beijing which includes 722 lines and 5421 bus station. In the first phase of the approach, space L is used. The results show that the bus network of Beijing is a scale-free network and the degree of more than 99 percent of nodes is lower than 10. The results also show that the network is an assortative network with 46 communities. In a second phase, space P is used to analyze the property of transfer. The results show that the average transfer time of Beijing bus network which is 1.88 and 99.8 percent of arbitrary two pair nodes is reachable within 4 transfers.

scholarly journals EVOLVING SCALE-FREE NETWORK MODEL WITH TUNABLE CLUSTERING

2005 ◽  
Vol 19 (26) ◽  
pp. 3951-3959 ◽  
Author(s):  
BING WANG ◽  
HUANWEN TANG ◽  
ZHONGZHI ZHANG ◽  
ZHILONG XIU
Keyword(s):  
Rate Equation ◽  
Continuum Theory ◽  
Clustering Coefficient ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
Law Degree ◽  
Analytical Expressions ◽  
Good Agreement ◽  
The Continuum

The Barabási–Albert (BA) model is extended to include the concept of local world and the microscopic event of adding edges. With probability p, we add a new node with m edges which preferentially link to the nodes presented in the network; with probability 1-p, we add m edges among the present nodes. A node is preferentially selected by its degree to add an edge randomly among its neighbors. Using the continuum theory and the rate equation method we get the analytical expressions of the power-law degree distribution with exponent γ=3 and the clustering coefficient c(k)~k-1+c. The analytical expressions are in good agreement with the numerical calculations.

PROPERTIES OF AN EVOLVING DIRECTED NETWORK WITH LOCAL RULES AND INTRINSIC VARIABLES

2007 ◽  
Vol 18 (01) ◽  
pp. 43-52
Author(s):  
ANDRZEJ GRABOWSKI ◽  
ROBERT A. KOSIŃSKI
Keyword(s):  
Network Evolution ◽  
Clustering Coefficient ◽  
Directed Network ◽  
Control Parameters ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
Regular Network ◽  
Intrinsic Variable ◽  
Connectivity Distribution

We present a simple model of an evolving directed network based on local rules. It leads to a complex network with the properties of real systems, like scale-free distribution of outgoing and incoming connectivity, and a hierarchical structure. Each node is characterised by an intrinsic variable S, and the number of outgoing links k out . As a result of network evolution the number of nodes and links (as well as their location) changes in time. For critical values of control parameters there is a transition to a scale-free network. Results for connectivity distribution found analytically agree with numerical calculations. Our model also reproduces other nontrivial properties of real networks, e.g. a large clustering coefficient and weak correlations between the age of a node and its connectivity. We have discovered an unexpected phenomenon that noise can increase the value of the clustering coefficient, whose large value is characteristic for a regular network.

Research on new evolution model of scale-free network

2009 ◽  
Vol 29 (5) ◽  
pp. 1230-1232
Author(s):  
Hao RAO ◽  
Chun YANG ◽  
Shao-hua TAO
Keyword(s):  
Evolution Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network
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