M-Centrality: identifying key nodes based on global position and local degree variation

Abstract

Identifying influential nodes in a network is a major issue due to the great deal of applications concerned, such as disease spreading and rumor dynamics. That is why, a plethora of centrality measures has emerged over the years in order to rank nodes according to their topological importance in the network. Local metrics such as degree centrality make use of a very limited information and are easy to compute. Global metrics such as betweenness centrality exploit the information of the whole network structure at the cost of a very high computational complexity. Recent works have shown that combining multiple metrics is a promising strategy to quantify the node's influential ability. Our work is in this line. In this paper, we introduce a multi-attributes centrality measure called M-Centrality that combines the information on the position of the node in the network with the local information on its nearest neighborhood. The position is measured by the K-shell decomposition, and the degree variation in the neighborhood of the node quantifies the influence of the local context. In order to examine the performances of the proposed measure, we conduct experiments on small and large scale real-world networks from the perspectives of transmission dynamics and network connectivity. According to the empirical results, the M-Centrality outperforms its alternatives in identifying both influential spreaders and nodes essential to maintain the network connectivity. In addition, its low computational complexity makes it easily applied to large scale networks.

Interplay Between Hierarchy and Centrality in Complex Networks

“…While these measures are usually linked to a single topological property of the network, recent works turn to multidimensional definitions. In this case, various complementary scalar topological properties of the network are combined to quantify the influence of the nodes [7,[34][35][36].Complexity is also an important issue of centrality measurement. Measures can be classified as local or global depending of the information used.…”

Impact of Seasonal Conditions on Vector-Borne Epidemiological Dynamics

“…There are many ways to introduce heterogeneity in the interactions of the host population. We choose to use the Barabasi -Albert model for the simulations, as it is the most influential model that allows accounting for heterogeneity in the complex network literature [20], [21], [62], [63].…”

Neighborhood-based bridge node centrality tuple for complex network analysis

“…Like the previously discussed metrics, the DCL metric also suffers from the weakness of combining multiple terms as part of its formulation in order to get computed as a scalar value. Some of the other related approaches (primarily in the context of identifying the most influential nodes for spreading information) that exist in the literature are as follows: Ibnoulouafi et al (2018), the authors propose to quantify the spreading capability of a node (referred to as M-Centrality) as a weighted average of the K-shell index number of the node (computed as part of K-shell decomposition (Wang et al 2016)) and the degree variation in the neighborhood of the node; the weight for computing the M-Centrality metric is determined by applying the entropy technique of He et al (2016) on the K-shell index numbers for the nodes and the extent of variation in node degree compared to the degrees of the neighbor nodes. The K-shell decomposition approach is likely to assign higher index numbers for nodes that are well-connected within a community (like node 4 in Fig.…”