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We considered a connection (edge) between two physiological measures (nodes) to exist if the correlation between them was statistically significant.
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New applications may be developed in the future, considering all the capabilities of the sensor nodes to measure physical variables.
If the number of radio nodes is large, measuring node-to-node link gains can be prohibitive.
Every 500 cycles was taken as a measurement node to measure the transmittance deviation of modified devices over time.
In [5], a distributed localization scheme is proposed, and it uses the target node to measure the TDOA.
To quantify local connectivity, the brain network measures "node degree" and "betweenness centrality" were selected.
The local graph theoretical measures node degree and betweenness centrality were derived for this AI ROI for each hemisphere.
Classical graph-based agglomerative methods employ a variety of similarity measures between nodes to partition PPI networks, but they often result in a poor clustering arrangement that contains one or a few giant core clusters with many tiny ones [1].
In this paper, we present a novel approach to detect injection attacks by modeling SQL queries as graph of tokens and using the centrality measure of nodes to train a Support Vector Machine (SVM).
We introduce two measures, node and edge cutoffs, to control for noise in the network (see Methods section).
The clustering coefficient measures the extent of nodes to cluster together by calculating the number of triangles in the network.
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