Analysis of the effect of node centrality on diffusion mode in complex networks

Su Zhen;  Gao Chao;  Li Xiang-Hua

doi:10.7498/aps.66.120201

Analysis of the effect of node centrality on diffusion mode in complex networks

Acta Physica Sinica ◽

10.7498/aps.66.120201 ◽

2017 ◽

Vol 66 (12) ◽

pp. 120201

Author(s):

Su Zhen ◽

Gao Chao ◽

Li Xiang-Hua

Keyword(s):

Complex Networks ◽

Diffusion Mode ◽

Node Centrality

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Temporal node centrality in complex networks

Physical Review E ◽

10.1103/physreve.85.026107 ◽

2012 ◽

Vol 85 (2) ◽

Cited By ~ 123

Author(s):

Hyoungshick Kim ◽

Ross Anderson

Keyword(s):

Complex Networks ◽

Node Centrality

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TIME CENTRALITY IN DYNAMIC COMPLEX NETWORKS

Advances in Complex Systems ◽

10.1142/s021952591550023x ◽

2015 ◽

Vol 18 (07n08) ◽

pp. 1550023 ◽

Cited By ~ 9

Author(s):

EDUARDO C. COSTA ◽

ALEX B. VIEIRA ◽

KLAUS WEHMUTH ◽

ARTUR ZIVIANI ◽

ANA PAULA COUTO DA SILVA

Keyword(s):

Diffusion Process ◽

Complex Networks ◽

Diffusion Processes ◽

Temporal Evolution ◽

Dynamic Network ◽

Time Varying ◽

Relative Importance ◽

Node Centrality ◽

Centrality Metrics ◽

Efficient Diffusion

There is an ever-increasing interest in investigating dynamics in time-varying graphs (TVGs). Nevertheless, so far, the notion of centrality in TVG scenarios usually refers to metrics that assess the relative importance of nodes along the temporal evolution of the dynamic complex network. For some TVG scenarios, however, more important than identifying the central nodes under a given node centrality definition is identifying the key time instants for taking certain actions. In this paper, we thus introduce and investigate the notion of time centrality in TVGs. Analogously to node centrality, time centrality evaluates the relative importance of time instants in dynamic complex networks. In this context, we present two time centrality metrics related to diffusion processes. We evaluate the two defined metrics using both a real-world dataset representing an in-person contact dynamic network and a synthetically generated randomized TVG. We validate the concept of time centrality showing that diffusion starting at the best ranked time instants (i.e., the most central ones), according to our metrics, can perform a faster and more efficient diffusion process.

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