Temporal Gravity Model for Important Nodes Identification in Temporal Networks

  title={Temporal Gravity Model for Important Nodes Identification in Temporal Networks},
  author={Jialin Bi and Jimmy Jin and Cunquan Qu and Xiuxiu Zhan and Guanghui Wang},

Vital node identification in hypergraphs via gravity model

Results show that a new centrality method (HGC) on the basis of the gravity model as well as a semi-local HGC, which can achieve a balance between accuracy and computational complexity, can filter out nodes that have fast spreading ability and are vital in terms of hypergraph connectivity.

Node-level Resilience Analysis for Temporal Networks based on K-shell Gravity

A node importance assessment model combining temporal $k$-shell decomposition and gravity is proposed, which inscribed the local structural and global characteristics of nodes using the temporal $ k-shell and the temporal distance respectively.

Identifying influential spreaders in complex networks by an improved gravity model

A high-resolution index combining both degree centrality and the k-shell decomposition method is proposed and, based on the proposed index and the well-known gravity law, an improved gravity model is proposed to measure the importance of nodes in propagation dynamics.



Temporal information gathering process for node ranking in time-varying networks.

This study proposed a temporal information gathering (TIG) process for temporal networks that can degenerate to classic metrics by a proper combination of these four variables and observed that the fastest arrival distance based TIG-process (fad-tig) is performed optimally in quantifying nodes' efficiency and nodes' spreading influence.

Path lengths, correlations, and centrality in temporal networks

  • R. K. PanJ. Saramäki
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2011
Differences between static and temporal properties are further highlighted in studies of the temporal closeness centrality, and correlations and heterogeneities in the underlying event sequences affect temporal path lengths, increasing temporal distances in communication networks and decreasing them in the air transport network.

Temporal node centrality in complex networks.

A simple but powerful model, the time-ordered graph, is presented, which reduces a dynamic network to a static network with directed flows, which enables it to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case.

Information diffusion backbones in temporal networks

This work proposes the construction of an information diffusion backbone GB(β) for a SI spreading process with an infection probability β on a temporal network and explores node pairs with what local connection features tend to appear in GB( β = 1), thus actually contribute to the global information diffusion.

The fundamental advantages of temporal networks

It is demonstrated that temporal networks can be controlled more efficiently and require less energy than their static counterparts, and have control trajectories that are considerably more compact than those characterizing static networks.

A Novel Entropy-Based Centrality Approach for Identifying Vital Nodes in Weighted Networks

This paper introduces a novel definition of entropy-based centrality, which can be applicable to weighted directed networks, and uses four weighted real-world networks with various instance sizes, degree distributions, and densities to evaluate the performance of this definition.

Measures of node centrality in mobile social networks

This paper first uses the temporal evolution graph model, which can more accurately capture the topology dynamics of the mobile social network over time, to redefine three common centrality metrics: degree centrality, closeness centrality and betweenness centrality.

Identifying influential spreaders by gravity model

A gravity model that utilizes both neighborhood information and path information to measure a node’s importance in spreading dynamics is proposed that performs very competitively in comparison with well-known state-of-the-art methods.

Temporal network structures controlling disease spreading.

This study concurs that long-time temporal structures, like the turnover of nodes and links, are the most important for the spreading dynamics.

Identifying influential nodes in complex networks with community structure