Iterative resource allocation for ranking spreaders in complex networks

Abstract

-Ranking the spreading influence of nodes in networks is a very important issue with wide applications in many different fields. Various topology-based centrality measures have been proposed to identify influential spreaders. However, the spreading influence of a node is usually not only determined by its own centrality but also largely influenced by the centrality of neighbors. To incorporate the centrality information of neighbors in ranking spreaders, we design an iterative resource allocation (IRA) process in which the resource of nodes distributes to their neighbors according to neighbors' centrality. After iterations, the resource amount on each node will be stable and the final resources of nodes are used to rank their spreading influence. The iterative process can be applied to many traditional centrality measures including degree, K-shell, closeness, and betweenness. The validation of our method is based on the susceptible-infectedrecovered (SIR) spreading in four representative real datasets. The results show that the ranking accuracy of the traditional centrality measures is remarkably enhanced by IRA.

Evolution properties of the community members for dynamic networks

“…In addition, tracing the community evolution is a challenge work, using the Markov process to describe the evolution of community structure [29] is an important method for this problem. Then, during the community evolution, how to explore the importance of nodes for dynamic networks [30][31][32][33] is also important problem to understand deeply the structure of networks. Finally, considering the characteristics of real networks, many of the networks are directed and the number of communities is unknown.…”

A dynamical approach to identify vertices' centrality in complex networks

“…Vertices are more tightly connected with each other in the same group, meanwhile there also exist some overlapping vertices belonging to more than one group. Further, in order to quantify the validity of possible subdivisions, we use the concept of modularity Q as a function form [32,33] (10) where a i = j e ij is the total fraction of links with one vertex in community i in network and e ij is the fraction of all links that link vertices in community i to vertices in community j. The modularity Q is a practical index to assess a given division into any number of communities for a given network.…”

“…Take degree centrality for example, vertices with larger degree have an ability to influence more other vertices. To monitor those influential vertices is helpful for the prediction and control of spreading dynamics [9,10].…”

Ranking Spreaders in Complex Networks Based on the Most Influential Neighbors

“…It is conducive to containing epidemic spread, studying information dissemination, and controlling virus diffusion [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. In this view, researchers have developed various methods, such as degree centrality (DC) [15], betweenness centrality (BC) [16], closeness centrality (CC) [17], and semilocal centrality [18] to identify the most influential spreaders in a network.…”