Semisupervised Classification Through the Bag-of-Paths Group Betweenness

Lebichot, Bertrand; Kivimäki, Ilkka; Françoisse, Kevin; Saerens, Marco

doi:10.1109/tnnls.2013.2290281

Cited by 10 publications

(3 citation statements)

References 25 publications

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“…The sensitivity analysis derivations presented in this work can provide insights in different applications of RSPs, such as movement modelling on networks, where the results can help in pinpointing critical and vital parts of a network when modelling movement on the network. In addition, this work can also be of use when applying the RSP framework in semi-supervised classification of network nodes (Kivimáki et al 2014, Lebichot et al 2014, Françoisse et al 2017. On the one hand, the sensitivity analyses can help, e.g.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…in detecting nodes or parts of a network that lie on boundaries of clusters or are outliers. Furthermore, the negative covariance term, σ st ij , introduced in this work in section 3 could also be harnessed directly for betweenness-based classification of graph nodes in the spirit of (Lebichot et al 2014), as well as for other applications of graph node centrality.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…shortest, or least-cost) paths but is monitored by an inverse temperature parameter β which introduces a degree of randomness in the behavior. This Gibbs-Boltzmann distribution elicits several graph measures of interest for various network applications, including distance and similarity measures between nodes (Yen et al 2008, Kivimáki et al 2014, Françoisse et al 2017 and network centrality measures (Lebichot et al 2014, Kivimáki et al 2016 among others.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Sensitivity to network perturbations in the randomized shortest paths framework: theory and applications in ecological connectivity

Kivimäki,

Van Moorter,

Saerens

2024

J. Phys. Complex.

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The Randomized Shortest Paths (RSP) framework, developed for network analysis, extends traditional proximity and distance measures between two nodes, such as shortest path distance and commute cost distance (related to resistance distance). Consequently, the RSP framework has gained popularity in studies on landscape connectivity within ecology and conservation, where the behavior of animals is neither random nor optimal.In this work, we study how local perturbations in a network affect proximity and distance measures derived from the RSP framework. For this sensitivity analysis, we develop computable expressions for derivatives with respect to weights on the edges or nodes of the network. Interestingly, the sensitivity of expected cost to edge or node features provides a new signed network centrality measure, the negative covariance between edge/node visits and path cost, that can be used for pinpointing strong and weak parts of a network. It is also shown that this quantity can be interpreted as minus the endured expected detour (in terms of cost) when constraining the walk to pass through the node or the edge.Our demonstration of this framework focuses on a migration corridor for wild reindeer (Rangifer rangifer) in Southern Norway. By examining the sensitivity of the expected cost of movement between winter and calving ranges to perturbations in local areas, we have identified priority areas crucial for the conservation of this migration corridor. This innovative approach not only holds great promise for conservation and restoration of migration corridors, but also more generally for connectivity corridors between important areas for biodiversity (e.g. protected areas) and climate adaptation. Furthermore, the derivations and computational methods introduced in this work present fundamental features of the RSP framework. These contributions are expected to be of interest to practitioners applying the framework across various disciplines, ranging from ecology, transport and communication networks to machine learning.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Discussion and Future Workmentioning

confidence: 99%