We explore the robustness of a network against failures of vertices or edges where a fraction of vertices is removed and an overload model based on betweenness is constructed.It is assumed that the load and capacity o...We explore the robustness of a network against failures of vertices or edges where a fraction of vertices is removed and an overload model based on betweenness is constructed.It is assumed that the load and capacity of vertex are correlated with its betweenness centrality B_(i)as B_(i)^(θ)and(1+α)B_(i)^(θ)(θis the strength parameter,αis the tolerance parameter).We model the cascading failures following a local load preferential sharing rule.It is found that there exists a minimal whenθis between 0 and 1,and its theoretical analysis is given.The minimalα_(c)characterizes the strongest robustness of a network against cascading failures triggered by removing a random fraction f of vertices.It is realized that the minimalα_(c)increases with the increase of the removal fraction f or the decrease of average degree.In addition,we compare the robustness of networks whose overload models are characterized by degree and betweenness,and find that the networks based on betweenness have stronger robustness against the random removal of a fraction f of vertices.展开更多
Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that...Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that traditional partitioning algorithms are designed for random networks and regular networks, rather than for scale-free networks. Multilevel graph-partitioning algorithms are currently considered to be the state of the art and are used extensively. In this paper, we analyse the reasons why traditional multilevel graph-partitioning algorithms perform poorly and present a new multilevel graph-partitioning paradigm, top down partitioning, which derives its name from the comparison with the traditional bottom-up partitioning. A new multilevel partitioning algorithm, named betweenness-based partitioning algorithm, is also presented as an implementation of top-down partitioning paradigm. An experimental evaluation of seven different real-world scale-free networks shows that the betweenness-based partitioning algorithm significantly outperforms the existing state-of-the-art approaches.展开更多
基金the National Natural Science Foundation of China(Grant Nos.71771186,71631001,and 72071153)the Natural Science Foundation of Shaanxi Province,China(Grant Nos.2020JM-486 and 2020JM-486).
文摘We explore the robustness of a network against failures of vertices or edges where a fraction of vertices is removed and an overload model based on betweenness is constructed.It is assumed that the load and capacity of vertex are correlated with its betweenness centrality B_(i)as B_(i)^(θ)and(1+α)B_(i)^(θ)(θis the strength parameter,αis the tolerance parameter).We model the cascading failures following a local load preferential sharing rule.It is found that there exists a minimal whenθis between 0 and 1,and its theoretical analysis is given.The minimalα_(c)characterizes the strongest robustness of a network against cascading failures triggered by removing a random fraction f of vertices.It is realized that the minimalα_(c)increases with the increase of the removal fraction f or the decrease of average degree.In addition,we compare the robustness of networks whose overload models are characterized by degree and betweenness,and find that the networks based on betweenness have stronger robustness against the random removal of a fraction f of vertices.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(Grant Nos.61003082 and 60903059)the National Natural Science Foundation of China(Grant No.60873014)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(Grant No.60921062)
文摘Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that traditional partitioning algorithms are designed for random networks and regular networks, rather than for scale-free networks. Multilevel graph-partitioning algorithms are currently considered to be the state of the art and are used extensively. In this paper, we analyse the reasons why traditional multilevel graph-partitioning algorithms perform poorly and present a new multilevel graph-partitioning paradigm, top down partitioning, which derives its name from the comparison with the traditional bottom-up partitioning. A new multilevel partitioning algorithm, named betweenness-based partitioning algorithm, is also presented as an implementation of top-down partitioning paradigm. An experimental evaluation of seven different real-world scale-free networks shows that the betweenness-based partitioning algorithm significantly outperforms the existing state-of-the-art approaches.
