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Direct or undirect graph type

Postby canikmata » 16 Jan 2014 23:14

Hi everyone, I am studying on research articles' keywords. I am trying to build up articles keywords (like descriptor) network. Especially keyword co-occurence. I prepare a .net file via a bibliometric analysis program. while generating .net file for keyword co-occurence, I assumed not a directed arcs. Is it a undirect or direct? Should I use undirected or directed graph type for Gephi?
Thanks
Last edited by canikmata on 20 Jan 2014 20:55, edited 1 time in total.
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Re: Direct or undirect graph type

Postby canikmata » 17 Jan 2014 11:50

I have used undirected graph type according to several reserach article which uses similar data and same method
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Re: Direct or undirect graph type

Postby francis_flavin » 17 Jan 2014 18:03

It probably depends on what sort of questions you want to ask of the graph. I have found it useful to use digraphs for some types of analyses and undirected graphs for other types.
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Re: Direct or undirect graph type

Postby seinecle » 20 Jan 2014 03:19

Cooccurrences are most commonly undirected.
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Re: Direct or undirect graph type

Postby canikmata » 20 Jan 2014 16:56

Thank you for your all comments and help.

I have searched the issue for few days,

I have read some articles and find good and detailed explanation of which graphic type I can use. I want share the information with you.

"....When the co-occurrence graph is directed, it means that
one type of entity is responsible for generating another type,
therefore it is logical to define the generative model as a
conditional probability. In the case of an undirected graph,
the joint probability is more appropriate.

Based on the categories for the co-occurrence data, four
different classes can be defined. In hetero-directed, each token
consists of two different entities and one type of entity is
responsible for generating the other. The most popular example
of this type is text data where a document is responsible
for generating words. Therefore, the link direction is from
document nodes to word nodes. In hetero-undirected, each
token consists of two different entities and both entities
are generated simultaneously from a joint distribution. An
example of this type is the co-occurrence of image features
and keywords.


Bibliographic Information:
Mohammad Khoshneshin, W. Nick Street, and Padmini Srinivasan. Bayesian embedding of co-occurrence data
for query-based visualization. In Proceedings of IEEE International Conference on Machine Learning and
Applications, 2011.
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