Betweenness centrality in large complex networks

نویسنده

  • M. Barthélemy
چکیده

We analyze the betweenness centrality (BC) of nodes in large complex networks. In general, the BC is increasing with connectivity as a power law with an exponent η. We find that for trees or networks with a small loop density η = 2 while a larger density of loops leads to η < 2. For scale-free networks characterized by an exponent γ which describes the connectivity distribution decay, the BC is also distributed according to a power law with a non universal exponent δ. We show that this exponent δ must satisfy the exact bound δ ≥ (γ + 1)/2. If the scale free network is a tree, then we have the equality δ = (γ + 1)/2. PACS. 89.75.-k Complex systems – 89.75.Hc Networks and genealogical trees – 05.40.-a Fluctuation phenomena, random processes, noise, and Brownian motion

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Fast Approach to the Detection of All-Purpose Hubs in Complex Networks with Chemical Applications

A novel algorithm for the fast detection of hubs in chemical networks is presented. The algorithm identifies a set of nodes in the network as most significant, aimed to be the most effective points of distribution for fast, widespread coverage throughout the system. We show that our hubs have in general greater closeness centrality and betweenness centrality than vertices with maximal degree, w...

متن کامل

Application of Group Testing in Identifying High Betweenness Centrality Vertices in Complex Networks

Group testing is a mathematical technique that uses superimposed code theory to find a specified number of distinct units among a large population, using the fewest number of tests. In this paper, we investigate the applicability of group testing in finding vertices with high betweenness centrality. Betweenness centrality (BC) is a widely applied network analysis objective, for identifying impo...

متن کامل

Efficient and Accurate Robustness Estimation for Large Complex Networks

Robustness estimation is critical for the design and maintenance of resilient networks, one of the global challenges of the 21st century. Existing studies exploit network metrics to generate attack strategies, which simulate intentional attacks in a network, and compute a metric-induced robustness estimation. While some metrics are easy to compute, e.g. degree centrality, other, more accurate, ...

متن کامل

Fast Computing Betweenness Centrality with Virtual Nodes on Large Sparse Networks

Betweenness centrality is an essential index for analysis of complex networks. However, the calculation of betweenness centrality is quite time-consuming and the fastest known algorithm uses O(N(M + N log N)) time and O(N + M) space for weighted networks, where N and M are the number of nodes and edges in the network, respectively. By inserting virtual nodes into the weighted edges and transfor...

متن کامل

Efficient Approximate Computation of Betweenness Centrality

Betweenness Centrality (BC) is a powerful metric for identifying central nodes in complex network analysis, but its computation in large and dynamic systems is costly. Most of the previous approximations for computing BC are either restricted to only one type of networks, or are too computationally inefficient to be applied to large or dynamically changing networks. We explore two approximative...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2003