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Dubbed "Plot Realignment Pullover" by the family! Many different yarns were used for the module plots, all 49 of them having a different pattern. Box-type pattern, there is no front or back.
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Many real-world networks can be represented as large graphs. Computational manipulation of such large graphs is common, but current tools for graph visualization are limited to datasets of a few thousand nodes.
These graphs contain sets of highly connected nodes that we call “communitiesâ€Â. Furthermore, these communities often have their own parts which are more connected than the rest that can be viewed as “sub-communitiesâ€Â. We used the Louvain method to extract communities and sub-communities from a sample network obtained from Arxiv dataset. We also used GUESS which is a graph exploration tool that contains an interpreted language (Gython) combined with a graphical front-end.
Using extracted hierarchical clustering dendrogram from Louvain method, we developed a tool which visualizes different hierarchical partitions of graph. Also, it allows us to manually merge and unmerge nodes into and from a community.
The plot shows the five levels of the decomposition, the smallest graph being the one between the communitiues whose decomposition maximizes the modularity according to Louvain method.
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Computing the diameter (i.e. the maximal distance between two nodes) of a huge graph is in many cases too time-consuming to be performed.
In Fast Computation of Empirically Tight Bounds for the Diameter of Massive Graphs we propose several methods to obtain upper and lower bounds for the diameter of a given graph. Although these bounds are guaranteed (it is sure that the diameter is between the bounds), we do not know if the bounds are tight in general. We therefore computed them on a variety of real-world cases; in all of them, the results were excellent: the difference between the upper and lower bounds was a few units only. As the computations needed to compute these bounds are very efficient, this provides an effective solution for diameter estimations when exact computation is out of reach (or not needed).
Our methods to compute upper and lower bounds are not deterministic: if we run them several times, we may obtain different values. As, in all cases, it is guaranteed that the diameter is indeed between the bounds, one may cumpute several bounds and keep the best ones.
The plot above represents the distributions of the obtained bounds for each method, ran 2000 times each on a web graph of approximately 40 million nodes and 800 million links. Each line corresponds to a different method (detailed in the paper)). For upper bounds (the three rightmost lines), we plot for each value x on the horizontal axis the number of times that the method output a bound larger than or equal to x. For lower bounds (the two leftmost lines) we plot for each value x on the horizontal axis the number of times that the method output a bound smaller than or equal to x.
In this case, we therefore reach the conclusion that our 40 million node graph has a diameter between 32 and 33 (it is thereforre 32 or 33, exactly). This is a very precise result, typical of this approach. It is obtained in minutes, while classical methods are unable to handle such massive graphs even within weeks.
Plot 53: John David McConnochie (85) 5/1/1990 – Rtd Cabinetmaker
Alice Pearl McConnochie (91) 6/3/1998
In Memory Of
JOHN DAVID McCONNOCHIE
1904 – 1990
and
ALICE PEARL McCONNOCHIE
1908 – 1998
Loving Parents
My plot in the winter snow. My strawberries are in the blue tub, and there was oregano on tan pot, the back bed has garlic.
On Maiden Tor below Sheepstor on Dartmoor Mark Fenlon takes focus on the PCWW stone that is dated 1919. View downhill towards Burrator Lake.
Mark and I plotted our next move whilst waiting for some pizza at Basil's. There are few better places to plot moves and wait for pizza than Basil's. If you've got schemes to brew and time to kill, I highly recommend it.
To see where on a map i find is fun, in a geeky investigative sort of way ;)
Not very often we get to get a glimpse into where a particular bird has been, and therefore have a good idea of probability as to where it might be going.
It would seem this particular Caspian Tern was likely heading back south after nesting this year, at same location where it was hatched.or ??....
Please feel free to comment, or share your thoughts and insights.
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Plots dotting the landscape...don't remember exactly where this was taken...somewhere between T.O and Denver.
So now if I could just dig under that fence I'd be free!!! Meerkats are perhaps THE most photogenic animals. But somehow they always look like they are plotting something :)
My car (in black) is sitting on the side street between the plot and my neighbours (shown on Vorplan as WEG)
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