## 9 Citations

### Percolation of a random network by statistical physics method

- Computer Science, PhysicsInternational Journal of Modern Physics C
- 2019

An analytical framework is developed and an exact mapping relation between a random network and Ising model is established and the partition function of the random network is obtained and used to compute the size of the giant component and the critical value of the present probability.

### Robustness of the complex networks by statistical physics method

- Computer Science2017 36th Chinese Control Conference (CCC)
- 2017

An exact mapping relation is established between ER networks and Ising model and the partition function of the ER networks is obtained to determine the size of the giant component and the value of the critical edge present probability in ER networks.

### Anti-Fragmentation of Resting-State Functional Magnetic Resonance Imaging Connectivity Networks with Node-Wise Thresholding

- Computer ScienceBrain Connect.
- 2017

It was found that networks constructed with hard thresholding included a large number of disconnected nodes, while such network fragmentation was not observed in networks formed with node-wise thresholding, indicating that node- wise thresholding may lead to less fragmented networks.

### Robustness of Air Transportation as Complex Networks:Systematic Review of 15 Years of Research and Outlook into the Future

- Business
- 2021

This review paper synthesizes the related literature that has been published throughout the last 15 years regarding the robustness of air transportation systems and provides a survey-style overview that hopefully contributes toward a better understanding of the state of the art in this research area, and to the improvement of future air transportation resilience and sustainability.

### Nonlinear statistical properties of fMRI signals in human visual cortex during resting-state

- BiologyPhysics Letters A
- 2018

### Relative differences in resting-state brain connectivity associated with long term intensive lifestyle intervention

- PsychologyPsychoneuroendocrinology
- 2016

### Revisiting giraﬀe photo-identiﬁcation using deep learning and network analysis

- Computer Science
- 2020

This work developed an end-to-end pipeline to retrieve a comprehensive set of re-identiﬁed giraﬀes from about 4, 000 raw photographs, and combined CNN-based object detection, SIFT pattern matching, and image similarity networks to retrieve the identity of known and unknown individuals.

### Revisiting animal photo‐identification using deep metric learning and network analysis

- Computer ScienceMethods in Ecology and Evolution
- 2021

This work developed an end‐to‐end pipeline to retrieve a comprehensive set of re‐identified giraffes from about 4,000 raw photographs, and quantified the performance of deep metric learning to retrieve the identity of known individuals, and to detect unknown individuals never seen in the previous years of monitoring.

### Revisiting giraffe photo-identification using deep learning and network analysis

- Computer SciencebioRxiv
- 2020

This work developed an end-to-end pipeline to retrieve a comprehensive set of re-identified giraffes from about 4, 000 raw photographs, which paves the way for further attempts to build CNN-based pipelines for re-identification of individual animals, in giraffe but also in other species.

## References

SHOWING 1-10 OF 28 REFERENCES

### Explosive Percolation in Random Networks

- Computer ScienceScience
- 2009

It is shown that incorporating a limited amount of choice in the classic Erdös-Rényi network formation model causes its percolation transition to become discontinuous.

### Using explosive percolation in analysis of real-world networks.

- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2011

It is shown with finite-size scaling that the university class, ordinary or explosive, of the resulting percolation transition depends on the structural properties of the network, as well as the number of unoccupied links considered for comparison in this procedure.

### Explosive Percolation Is Continuous

- PhysicsScience
- 2011

A mathematical proof shows that in many models of the growth of network connectivity, phase transitions are continuous, although related models in which the number of nodes sampled may grow with the network size can indeed exhibit explosive percolation.

### Construction and analysis of random networks with explosive percolation.

- Computer SciencePhysical review letters
- 2009

The underlying mechanism behind first-order phase transitions in random networks is described and tools that allow us to identify (and predict) when a random network will exhibit an explosive transition are developed.

### Explosive percolation in scale-free networks.

- Computer SciencePhysical review letters
- 2009

The Achlioptas growth process leads to a phase transition with a nonvanishing percolation threshold already for lambda>lambda(c) approximately 2.2.2, but the transition is continuous when lambda<or=3 but becomes discontinuous when lambda>3.

### Explosive percolation transition is actually continuous.

- PhysicsPhysical review letters
- 2010

A representative model is considered which shows that the explosive percolation transition is actually a continuous, second order phase transition though with a uniquely small critical exponent of thePercolation cluster size.

### Explosive percolation in the human protein homology network

- Biology
- 2010

The results indicate that the evolutionary-based process that shapes the topology of the H-PHN through duplication-divergence events may occur in sudden steps, similarly to what is seen in first-order phase transitions.

### Percolation on sparse networks

- Computer SciencePhysical review letters
- 2014

Percolation is reformulate as a message passing process and the resulting equations can be used to calculate the size of the percolating cluster and the average cluster size, finding them to be highly accurate when compared with direct numerical simulations.

### Predicting percolation thresholds in networks.

- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2015

This study reveals that the inverse of the largest eigenvalue of the nonbacktracking matrix of the graph often provides a tight lower bound for true percolation threshold, but in more than 40% of the cases, this indicator is less predictive than the naive expectation value based solely on the moments of the degree distribution.

### Explosive percolation: a numerical analysis.

- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2010

A detailed numerical analysis of Achlioptas processes with product rule on various systems, including lattices, random networks á la Erdös-Rényi, and scale-free networks shows that all relevant percolation variables display power-law scaling, just as in continuous second-order phase transitions.