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

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If the author or investigator you have searched has for a Google Scholar profile, you will see a link to their profile at the top of the results page:

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After you have specified the export format you can save the CSV file to your desired location by clicking the "Export all articles by Alessandro Vespignani" button. Save the file do to your desktop and then load it into Sci2  Sci2 in the standard CSV format:

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This will open the dataset in Excel (or you preferred spreadsheet editor). You will notice that the Lowercase, Tokenize, Stem, and Stopword Text algorithm has place brackets around the years. You will need to remove these before you can run the Burst Detection algorithm. In Excel, hit 'Ctrl-F' on the keyboard. This will bring up the Find and Replace tool. Highlight the column of years and then perform a find and replace:

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You will have to repeat this for the other bracket symbol. This will essentially allow you remove the brackets around the years. Next you will need to remove those publications for which there is no year information. Burst Detection will not run if there are empty values in the date column. You can search for the publications and find the proper date, but the year value could be empty because these are forthcoming publications. In this example, we will just remove all publications without a value in the year column:

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This will result in a "Burst detection analysis (Year, Title): maximum burst level 1" file in the data manager , right Right click on this file to view the data:

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You will need to edit the data before you can run the Temporal Bar Graph algorithm to visualize the results of the burst detection. First, you should make sure every record has an "End" date or else the Temporal Bar Graph will not run properly. We know that this dataset contains records that are labeled with the year of 2013, so that will be our end date for those bursts that are still continuing:

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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. So This means you can have a bar with a high weight value that appears thinner when , 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 "totalweight" column and assign strong, medium, and weak categories to these records based on the "totalweight" values:

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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:

Note , selecting that if you select the "Simplified Layout" option no legend will be created for the map, allowing . This allows you to create your own legend that will be accurate based creating new weight values. To learn how to create a legend for your visualization see 2.4 Saving Visualizations for Publication.

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Remember that the weight for the bars is equal to the total area, not simply the thickness. So, having including the color categories will help users make more sense of the visualization. You notice that this burst analysis for Alessandro 's Vesipignani's publications looks similar to the one created in the previous section. However, this new bust burst analysis takes into consideration his more recent publications and interests in human mobility networks and epidemiology. This workflow can easily be repeated using any author who has a profile in Google Scholar. Give it a try for yourself!

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