However, for such distributions to be meaningful descriptors of network structure, their underlying forms should for long enough intervals become stationary and depend only on their average values.
Graphlets can be used to derive detailed descriptors of network topology at network, node and edge level (Milenković and Pržulj, 2008; Solava et al., 2012; Yaveroğlu et al., 2014).
All annotations are available in the Additional file 3. Four categories of quantitative descriptors of network structure and complexity, based on the notions of degree, centrality, distance and cliques, were used to characterize the NIPs constructed to represent taxa (see Additional file 4 and associated references for details on the 52 descriptors used-25 basic and 27 derivatives).
In this contribution, the authors attempt to use the structural properties of metabolic networks to identify what domain of life they belong to, using a dataset of 43 previously published networks and a set of topological descriptors of the network, using 33 "descriptors" or features.
The model is analytically solved to calculate the performance descriptors of the network, mean backoff time to access the medium, average waiting time in queue, collision probability of a transmission, and normalized throughput, for all data classes.
To address this question, we introduced descriptors of vein network topology that enable quantification of vein number, connectedness and continuity, and combined these topological descriptors with cellular imaging and molecular genetic analysis to quantify the contribution of PIN1, PIN5, PIN6, and PIN8 to vein network topology.
For this purpose, we developed the novel descriptor description of network signal communication DNSC for catalytic residues to reveal the effects imposed on catalytic residues by other residues.
The authors of the current manuscript computed a large variety of topological descriptors of complex networks, many of which have been proposed only recently (some by the authors themselves), and applied various statistical machine learning techniques to find the descriptors or their combinations that best separate three domains of Life, i.e., Bacteria, Archaea and Eukarya.
In our case, this means that we use the values of the different topological network descriptors of the underlying metabolic network of an organism to predict which domain of life the organism belongs to.
Plateau and Atif showed that the SAN descriptor of a network consisting of k automata and having l synchronizing events can be expressed as follows: (10) Q = ∑ j = 1 k + 2 l ⨂ i = 1 g k Q j i.
In this study we empirically evaluated how variation in sampling effort (comparing cumulative vs monthly sampled networks) influences the values of network descriptors more frequently used in the literature (i.e. number of links per species, network size, diversity of interactions, network specialization, robustness, nestedness, and modularity).
