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In [10] Jachymski gave a more general unified version of these extensions by considering graphs instead of a partial order.
Hence, this feature could be useful (as future work) when considering graphs which were inferred statistically (erroneous graphs) [85].
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In considering graph metadata, the goal is to develop stratified graph sampling methods that balance graph structure directly against latent traits.
Then, considering graph components ( s), and denoting by the maximal in the network, that is,, we can have the polynomial time complexity for TMGA2 as.
In this section we shall consider graphs which, in a local sense, contain only a restricted number of P4's.
Using NetworkX primitives, we are able to compute the shortest paths on the considered graphs and compute the equations we have defined so far.
As an important remark, we want to emphasize that the uniqueness of a topological index also depends on the considered graphs class.
As a simple example consider graphs where nodes are labeled by either red or blue color.
Here, we consider graphs with one or more start-nodes, but with a single end-node.
When we consider graphs over three nodes X1, X2 and X3, the total number of possible network structures is 25.
In [ 10], Zhang and Wei extend the general network propagation algorithm to consider graphs with nodes and edges to be positive and negative numbers for the sake of detecting differential gene expressions and DNA copy number variations (CNV).
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