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Nagaraja et al. [67] have proposed a botnet detection technique based on structured graph analysis that localizes botnet members by identifying unique communication patterns arising from the overlay topologies prevalent in a command and control structure.
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However, it is different from our model as it is based on graph structures rather than group structures.
CS22 is based on graph structures, which have been suggested to possess the greatest benefit to effort ratio for conservation problems regarding landscape connectivity.
In this paper, we call graphs based on graph theory "network graphs".
The model is based on a graph structure representing scenarios of interest in terms of facts, processes, and technologies.
In contrast, Personalized PageRank, which is a variant of PageRank, assigns a biased jump probability to each node and ranks graph nodes based on the graph structure.
To help experts investigate mind wanderings, we present a graph-based approach for visual analytics of eye-tracking data, which utilizes the graph representations to illustrate the reading patterns and further help experts detect and verify mind wanderings based on the graph structures and other graph attributes.
GrAF is based on a graph structure, and BioC using relations can easily represent a graph.
The similarity measure proposed by Wang [ 32] is also implemented in the ppiPre package, which is based on the graph structure of GO DAG.
In the current work, we show analytically that a network rescoring method based on local graph structure will increase graph transitivity in an unweighted network, provided that it is not too far from a network of cliques.
We give a computational problem formulation for correcting errors in contigs, and present an algorithm for reassembly based on a graph structure referred to as the positional de Bruijn graph.
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