Sentence examples for temporal paths from inspiring English sources

We emphasize that most previous research focused on the first modeling approach (i.e. long-term) whereas only a few studies look at high-resolution temporal paths.

We conclude the review highlighting that much research remains to be done, both to apply already available methods and to develop new measures for temporal paths on air transport networks.

We design a deterministic algorithm to compute a form of temporal betweenness in time-varying graphs (foremost betweenness) that measures centrality of nodes in terms of how often they lie within temporal paths with the earliest arrival.

We distinguish the structural analysis of sequences of network snapshots, ideal for long-term network evolution (e.g. annual evolution), and temporal paths, preferred for short-term dynamics (e.g. hourly evolution).

We introduce a tool, TimeXNet (http://timexnet.hgc.jp/), which identifies active gene sub-networks with temporal paths using time-course gene expression profiles in the context of a weighted gene regulatory and protein-protein interaction network [ 8].

We introduce a tool, TimeXNet, which identifies active gene sub-networks with temporal paths using time-course gene expression profiles in the context of a weighted gene regulatory and protein-protein interaction network.

In 1916 Albert Einstein (1879 1955) published "The Foundation of the General Theory of Relativity," which replaced Newton's description of gravitation as a force that attracts distant masses to each other through Euclidean space with a principle of least effort, or shortest (temporal) path, for motion along the geodesics of a curved space.

Figure 9 Temporal path length analysis for 'Pharmaceuticals' R&D network (SIC code 283).

Figure 10 Temporal path length analysis for the co-authorship network in applied and interdisciplinary physics (PACS number 89).

Particular reference is made to spatial and temporal path dependencies and to the significance of cross-scale and cross-sector effects that impact the innovation process.

First, for every frame, several fundamental frequency candidates are predicted, and second, the most probable temporal path is estimated, according to a hidden Markov model.