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  • 5.2.5 Burst Detection in Physics and Complex Networks (ISI Data)

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Table of Contents

AlessandroVespignani.isi

 

Time frame:

1990-2006

Region(s):

Indiana University, University of Rome, Yale University, Leiden University, International Center for Theoretical Physics, University of Paris-Sud

Topical Area(s):

Informatics, Complex Network Science and System Research, Physics, Statistics, Epidemics

Analysis Type(s):

Burst Detection

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The "Gamma" parameter is the value that state transition costs are proportional to. This parameter is used to control how ease easy the automaton can change states. The higher the "Gamma" value, the smaller the list of bursts generated.

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The "Date Format" specifies how the date column will be interpreted as a date/time. See http://javadocs.sunoracle.com/j2se/1.4.2javase/6/docs/api/java/text/SimpleDateFormat.html for details.

The "Text Column" parameter is the name of the column with values (delimiter and tokens) to be computed for bursting results.

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And the "End" field indicates when the burst stopped. A An empty value in the "End" field indicates that the burst lasted until the last date present in the dataset. Where the "End" field is empty, put manually add the last year present in the dataset. In ; in this case, 2006.

After you manually add manually this information, save this .csv file somewhere in your computer. Load back this Reload the .csv file into Sci2 using 'File > Load'. Select 'Standart Standard csv format' int the pop-up window. A new table will appear in the Data Manager. To visualize these the table that contains the results of the Burst Detection algorithm, select the table you just loaded in the Data Manager and run 'Visualization > Temporal > Temporal Bar Graph' with the following parameters:

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Again where the "End" field is empty, put manually add the last year present in the dataset. In ; in this case, 2006.

After you manually add manually this information, save this .csv file somewhere in your computer. Load back Reload this .csv file into Sci2 using 'File > Load'. Select 'Standart Standard csv format' int the pop-up window. A new table will appear in the Data Manager. To visualize these table that contains these new results for the Burst Detection algorithm, select the table you just loaded in the Data Manager and run 'Visualization > Temporal > Horizontal Bar Graph (not included version)' with the same parameters.

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As expected, a larger number of bursts appear, and the new bursts have a smaller weight that those depicted in the first graph. These smaller, more numerous bursting terms permit a more detailed view of the dataset and allow the identification of trends. The "protein" burst starting in 2003, for example, indicates the year in which Alessandro Vespignani started to work with "protein-protein interaction networks," while the burst "epidem" - also from 2001 - is related to the application of complex networks to the analysis of epidemic phenomena in biological networks.

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5.2.5.2 Updating the Vespignani Dataset
5.2.5.2 Updating the Vespignani Dataset
5.2.5.2 Updating the Vespignani Dataset

The original dataset for Alessandro Vespignani was created in 2006. If you wish to update the dataset to gain an understanding for how his research has changed and evolved since 2006 you can obtain a new dataset from the from Web of Science, see 4.2.1.3 ISI Web of Science. However, another way to obtain an individual researcher's publication information is to use their Google Scholar profile, if they have one. One of the biggest benefits to using a Google Scholar profile is that you will get publications not indexed in Web of Science, such as some book chapters. In this example, we will obtain the publication information for Alessandro Vespignani using Google Scholar:

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Keep in mind that not every author you search will necessarily have a Google Scholar profile, but for those that do, this is a very useful way to get their publication information. Click on the link to view Alessandro Vespignani's profile, and then select all publications and click the export button at the top of his publication list to export the citation information:

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Before you can visualize the results with the Temporal Bar Graph it is important to know that if you want to size bars based on weight, the weight value will be distributed across the length of the burst. In other words, the total area of the bar corresponds to the weight value. This means you can have a bar with a high weight value that appears thinner, compared to bar with a lower weight value if the former burst occurs over a longer period than the latter. Finally, before you visualize this dataset, you can add some categories to allow you to color your bars. For example you can sort the records from largest to smallest based on the "totalweighttotal weight" column and assign strong, medium, and weak categories to these records based on the "totalweighttotal weight" values:

Now, save the file to your desktop and reload it into Sci2 in the standard CSV format and run 'Visualization > Temporal > Temporal Bar Graph', entering the following parameters:

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