Could you please let me know how to view the list of papers that was tagged for keywords with the strongest citation burst. I don't mean to view the members of a cluster, but of a keywords. I have a list of 200 keywords.
Thanks
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Thanks Prof. Chen for your answer. I can now view the members of a node by keyword. Can I view the members of a keyword in the list of keywords on the left hand side, which do not form a node.
Regards
Each keyword on the list is a node in the network. By default, the largest connected component of the network is shown. If a node is not seen, you can unselect the default: Filters > Show the largest connected component.
There are several ways to locate a particular node from the list:
uncheck/check the check box on the list and see node changes in the graph, or
use the search field, type the node name
Once you locate the node, follow the right-click option.
Last edit: Chaomei Chen 2021-01-04
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
Could you please let me know how to view the list of papers that was tagged for keywords with the strongest citation burst. I don't mean to view the members of a cluster, but of a keywords. I have a list of 200 keywords.
Thanks
Node type=keywords, right-click on a keyword node, the list is there.
Thanks Prof. Chen for your answer. I can now view the members of a node by keyword. Can I view the members of a keyword in the list of keywords on the left hand side, which do not form a node.
Regards
Each keyword on the list is a node in the network. By default, the largest connected component of the network is shown. If a node is not seen, you can unselect the default: Filters > Show the largest connected component.
There are several ways to locate a particular node from the list:
uncheck/check the check box on the list and see node changes in the graph, or
use the search field, type the node name
Once you locate the node, follow the right-click option.
Last edit: Chaomei Chen 2021-01-04
It's clear to me now. Thanks Prof. Chen.