Many thanks for your input Tom. An in-degree is the number of incoming
edges towards that node. I think that captures *influencer: influencee
(Aristotle : Alexander, Aristotle : Myself)*, which means, in this
scenario, Aristotle (the node), has an in-degree of 2. I thought in-degree
was a measure of influence rather than an outdegree. Remember, this is
going to be plotted as a *directed* graph in Gephi. I'll be curious to know
how I'll actually distinguish in-degrees and out-degrees in Gephi
practicaly, but anyway.
Moreover, I didn't quite get about how I could do a Page-Rank style style
aggregation on this specific scenario. Could you please provide some
examples using actual person names so I can digest it well in my head.
Thanks for getting my head working though, but I still believe the
Wikipedia data gives you a decent impression of influence to an extent,
albeit not the most accurate, but it kind of appears to be right in one way
or the other.
On Sat, Dec 21, 2013 at 7:49 PM, Tom Morris <tfmor...@gmail.com> wrote:
> On Sat, Dec 21, 2013 at 2:24 PM, Ali Gajani <aligaj...@gmail.com> wrote:
>
>> ... I want to make sure I can use this dataset to count indegrees (high
>> influencers) properly. It is impossible to survey all the rows to ensure
>> the knowledge is true, but I am asking anyway.
>>
>
> Presumably you mean out-degree if you're talking about influencers. A
> simple count doesn't sound like it'll capture the real influence. Even if
> you assume that Wikipedia has a comprehensive and unbiased coverage of
> influential people (almost certainly not true), shouldn't influencing
> someone influential count more? That would imply you need to do a page-rank
> style aggregation of link weights.
>
> Tom
>
--
Ali Gajani
Founder at Mr. Geek
www.mrgeek.me
www.aligajani.com
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