"To understand, how noise is related to scale-freeness, we have to do some mathematics again. Noise is usually characterized by a mathematical trick. The seemingly random fluctuation of the signal is regarded as a sum of sinusoidal waves. The components of the million waves giving the final noise structure are characterized by their frequency. To describe noise, we plot the contribution (called spectral density) of the various waves we use to model the noise as a function of their frequency. This transformation is called a Fourier transformation [...]" (Péter Csermely, "Weak Links: The Universal Key to the Stabilityof Networks and Complex Systems", 2009)
"Inclusiveness simply measures the number or proportion of the whole set of points that are actually connected into one or more parts of the graph. Some points may be isolates, having no ties to other points, while others will be connected, to a greater or lesser extent, into larger structures. The inclusiveness of a graph is simply the total number of non-isolated points, gener-ally expressed as a percentage of the total number of points. Inclusiveness is a rough and ready approximation to cohesion, but it is usually more informative to measure the actual density of the graph." (John Scott," What is Social Network Analysis?", 2012)
"The density of a graph is a very useful and direct measure of its cohesion, but it has one major limitation as a comparative measure of social structure. In real situations, density varies with the size of a network and this limits the possibilities of using the measure to compare different types of network. It is highly unlikely that agents are able to sustain more than a certain number of relationships: our ability to be ‘friends’ with people, for example, has its limits." (John Scott," What is Social Network Analysis?", 2012)
"Linking is a powerful dynamic interactive graphics technique that can help us better understand high-dimensional data. This technique works in the following way: When several plots are linked, selecting an observation's point in a plot will do more than highlight the observation in the plot we are interacting with - it will also highlight points in other plots with which it is linked, giving us a more complete idea of its value across all the variables. Selecting is done interactively with a pointing device. The point selected, and corresponding points in the other linked plots, are highlighted simultaneously. Thus, we can select a cluster of points in one plot and see if it corresponds to a cluster in any other plot, enabling us to investigate the high-dimensional shape and density of the cluster of points, and permitting us to investigate the structure of the disease space." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)
"When using community detection algorithms, be conscious of the density of the relationships. If the graph is very dense, you may end up with all nodes congregating in one or just a few clusters. You can counteract this by filtering by degree, relationship weights, or similarity metrics. On the other hand, if the graph is too sparse with few connected nodes, you may end up with each node in its own cluster. In this case, try to incorporate additional relationship types that carry more relevant information." (Mark Needham & Amy E Hodler, "Graph Algorithms: Practical Examples in Apache Spark and Neo4j", 2019)
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