You Shall Not Pass: Avoiding Spurious Paths in Shortest-Path Based Centralities in Multidimensional Complex Networks

@article{Wehmuth2020YouSN,
  title={You Shall Not Pass: Avoiding Spurious Paths in Shortest-Path Based Centralities in Multidimensional Complex Networks},
  author={Klaus Wehmuth and Artur Ziviani and Leonardo Chinelate Costa and Ana Paula Couto da Silva and Alex Borges Vieira},
  journal={IEEE Transactions on Network Science and Engineering},
  year={2020},
  volume={8},
  pages={138-148}
}
The aggregation process may create spurious paths on the aggregated view of multidimensional (high order) complex networks. Consequently, these spurious paths may then cause shortest-path based centrality metrics to produce incorrect results, thus undermining the network centrality analysis. In this context, we propose a method built upon MultiAspect Graphs (MAGs) able to avoid taking into account spurious paths when computing centralities based on shortest paths in multidimensional complex… 

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References

SHOWING 1-10 OF 29 REFERENCES

Avoiding Spurious Paths in Centralities Based on Shortest Paths in High Order Networks

By using this MAG representation, pitfalls usually associated with spurious paths resulting from aggregation in time-varying and multilayer networks can be avoided and path-based centralities are assured to be computed correctly without taking into account spurious paths that could lead to incorrect results.

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.

Multidimensional networks: foundations of structural analysis

This paper presents a solid repertoire of basic concepts and analytical measures, which take into account the general structure of multidimensional networks, and tests the validity and the meaningfulness of the measures introduced, that are able to extract important and non-random information about complex phenomena in such networks.

Time Centrality in Dynamic Complex Networks

It is validated the concept of time centrality showing that diffusion starting at the best ranked time instants (i.e., the most central ones), according to the metrics, can perform a faster and more efficient diffusion process.

Foundations of Multidimensional Network Analysis

This paper develops a solid repertoire of basic concepts and analytical measures, which takes into account the general structure of multidimensional networks, and tests the validity and the meaningfulness of the measures introduced, that are able to extract important, nonrandom information about complex phenomena.

Centrality rankings in multiplex networks

The betweenness centrality measure is re-define to account for the inherent structure of multiplex networks and an algorithm to compute it in an efficient way is proposed and shown to be more accurate than the current approach.

A faster algorithm for betweenness centrality

New algorithms for betweenness are introduced in this paper and require O(n + m) space and run in O(nm) and O( nm + n2 log n) time on unweighted and weighted networks, respectively, where m is the number of links.

On variants of shortest-path betweenness centrality and their generic computation

Graph cube: on warehousing and OLAP multidimensional networks

Graph Cube is introduced, a new data warehousing model that supports OLAP queries effectively on large multidimensional networks and is shown to be a powerful and efficient tool for decision support on large multi-dimensional networks.

A unifying model for representing time-varying graphs

This work proposes a novel model for representing finite discrete Time-Varying Graphs (TVGs), which is an unifying model that can represent several previous models for dynamic networks found in the recent literature, and is able to intrinsically model cyclic behavior in dynamic networks.