New Method for Estimating the Tree-Likeness of Graphs and its Application for Tracing the Robustness of Complex Networks

2018 ◽  
Author(s):  
Artem Potebnia
Keyword(s):  
Complex Networks ◽  
New Method

COMMUNITY DETECTION IN NETWORKS

2010 ◽  
Vol 20 (02) ◽  
pp. 361-367 ◽  
Author(s):  
C. O. DORSO ◽  
A. D. MEDUS
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
New Method

The problem of community detection is relevant in many disciplines of science. A community is usually defined, in a qualitative way, as a subset of nodes of a network which are more connected among themselves than to the rest of the network. In this article, we introduce a new method for community detection in complex networks. We define new merit factors based on the weak and strong community definitions formulated by Radicchi et al. [2004] and we show that this local definition properly describes the communities observed experimentally in two typical social networks.

A New Method for Extracting the Hierarchical Organization of Networks

2017 ◽  
Vol 16 (05) ◽  
pp. 1359-1385 ◽  
Author(s):  
Weihua Zhan ◽  
Jihong Guan ◽  
Zhongzhi Zhang
Keyword(s):  
Complex Networks ◽  
Networked Systems ◽  
Hierarchical Organization ◽  
New Method ◽  
Network Data ◽  
Large Complex

Extracting the hierarchical organization of networks is currently a pressing task for understanding complex networked systems. The hierarchy of a network is essentially defined by the heterogeneity of link densities of communities at different scales. Here, we define a top-level partition (TLP) as a bipartition of the network (or a sub-network) such that no top-level community (TLC) runs across the two parts. It has been found that a TLP generally has a higher modularity than a non-top-level (TLP) partition when their TLCs have similar sizes and when the link densities of neighboring levels are well separated from each other. A spectral TLP procedure is proposed here to search for TLPs of a network (or sub-network). To extract the hierarchical organization of large complex networks, an algorithm called TLPA has been developed based on the TLP. Experiments have shown that the method developed in this research extract hierarchy accurately from network data.

ENTRAINMENT COMPETITION IN COMPLEX NETWORKS

2010 ◽  
Vol 20 (03) ◽  
pp. 827-833
Author(s):  
I. LEYVA ◽  
I. SENDIÑA-NADAL ◽  
J. A. ALMENDRAL ◽  
J. M. BULDÚ ◽  
D. LI ◽  
...  
Keyword(s):  
Complex Networks ◽  
Network Structure ◽  
New Method ◽  
Simultaneous Presence ◽  
Modular Network ◽  
Overlapping Communities ◽  
Frequency Changes

The response of a random and modular network to the simultaneous presence of two frequencies is considered. The competition for controlling the dynamics of the network results in different behaviors, such as frequency changes or permanent synchronization frustration, which can be directly related to the network structure. From these observations, we propose a new method for detecting overlapping communities in structured networks.

Universal resilience patterns in cascading load model: More capacity is not always better

2017 ◽  
Vol 28 (03) ◽  
pp. 1750041
Author(s):  
Jianwei Wang ◽  
Xue Wang ◽  
Lin Cai ◽  
Chengzhang Ni ◽  
Wei Xie ◽  
...  
Keyword(s):  
Complex Networks ◽  
Shortest Paths ◽  
Simple Graph ◽  
New Method ◽  
Cascading Failures ◽  
Load Model ◽  
Additional Load ◽  
Reasonable Explanation

We study the problem of universal resilience patterns in complex networks against cascading failures. We revise the classical betweenness method and overcome its limitation of quantifying the load in cascading model. Considering that the generated load by all nodes should be equal to the transported one by all edges in the whole network, we propose a new method to quantify the load on an edge and construct a simple cascading model. By attacking the edge with the highest load, we show that, if the flow between two nodes is transported along the shortest paths between them, then the resilience of some networks against cascading failures inversely decreases with the enhancement of the capacity of every edge, i.e. the more capacity is not always better. We also observe the abnormal fluctuation of the additional load that exceeds the capacity of each edge. By a simple graph, we analyze the propagation of cascading failures step by step, and give a reasonable explanation of the abnormal fluctuation of cascading dynamics.

A new method to identify influential nodes based on combining of existing centrality measures

2017 ◽  
Vol 31 (26) ◽  
pp. 1750243 ◽  
Author(s):  
Liguo Fei ◽  
Hongming Mo ◽  
Yong Deng
Keyword(s):  
Complex Networks ◽  
Lower Limit ◽  
Information Flow ◽  
New Method ◽  
Centrality Measures ◽  
Centrality Measure ◽  
Upper Limit ◽  
Open Issue ◽  
Novel Method ◽  
Influential Nodes

How to identify influential nodes in complex networks continues to be an open issue. A number of centrality measures have been presented to address this problem. However, these studies focus only on a centrality measure and each centrality measure has its own shortcomings and limitations. To solve the above problems, in this paper, a novel method is proposed to identify influential nodes based on combining of the existing centrality measures. Because information flow spreads in different ways in different networks, in the specific network, the appropriate centrality measures should be selected to calculate the ranking of nodes. Then, an interval can be generated for the ranking of each node, which includes the upper limit and lower limit obtained from different centrality measures. Next, the final ranking of each node can be determined based on the median of the interval. In order to illustrate the effectiveness of the proposed method, four experiments are conducted to identify vital nodes simulations on four real networks, and the superiority of the method can be demonstrated by the results of comparison experiments.

SELF-SIMILARITY IN COMPLEX NETWORKS: FROM THE VIEW OF THE HUB REPULSION

2013 ◽  
Vol 27 (28) ◽  
pp. 1350201 ◽  
Author(s):  
HAIXIN ZHANG ◽  
XIN LAN ◽  
DAIJUN WEI ◽  
SANKARAN MAHADEVAN ◽  
YONG DENG
Keyword(s):  
Fractal Dimension ◽  
Complex Systems ◽  
Complex Networks ◽  
Fractal Structure ◽  
The Self ◽  
New Method ◽  
Self Similarity ◽  
Nature And Society ◽  
Real Complex

Complex networks are widely used to model the structure of many complex systems in nature and society. Recently, fractal and self-similarity of complex networks have attracted much attention. It is observed that hub repulsion is the key principle that leads to the fractal structure of networks. Based on the principle of hub repulsion, the metric in complex networks is redefined and a new method to calculate the fractal dimension of complex networks is proposed in this paper. Some real complex networks are investigated and the results are illustrated to show the self-similarity of complex networks.

Sign in / Sign up

Export Citation Format

Share Document