Sentence examples for backbone extraction from inspiring English sources

Various backbone extraction algorithms have been proposed in different scientific fields.

Network backbone extraction techniques reduce the size of networks while trying to preserve their key topological and spatial features.

Although of clear interest to transport geographers, backbone extraction techniques have been adopted unevenly and in an ad hoc fashion in transport geography research.

In this paper we therefore present a conceptual and experimental comparison of backbone extraction techniques in a transport-geographical context, and explore the new insights each technique can offer to enhance our understanding of the Southeast Asian intercity air transport network (SAAN).

Reduction techniques like community detection or backbone extraction belong to this tradition; both techniques use the distribution of edges as the basis from where to identify the inner structure of a network [2], [3].

We call this method for backbone extraction locally adaptive network sparsification, or LANS.

Chang et al. [30] clustered the social relations among individuals using users' location information on the city scale to construct an intercity social relationship network and presented a geographic backbone network extraction method that combines gravity models and information entropy technologies.

The reduced tuning time for the excitation makes the commonly used free-decay measurements for the extraction of backbone curves unnecessary.

This paper presents a technique for the extraction of backbone curves of lightly damped nonlinear systems that is well suited for the experimental investigation of structures exhibiting nonlinear behaviour.

Among a number of possible methods from Graph Theory [91, 92, 153, 154], we opted for a novel technique based on the extraction of Simmelian backbones [155], due to its efficiency to analyze complex networks with unweighted edges.

As a result of the extraction of Simmelian backbones from the collaborative network of the last OpenStack releases, the emergent sub-communities in Fig. 13 reveal, contrary to what the authors expected, a low degree of homophily in code collaboration, meaning that developers do not tend to work with developers from their own company.