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CMN 150V Final Exam – Questions With Applicable Solutions

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CMN 150V Final Exam – Questions With Applicable Solutions

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CMN 150V Final Exam – Questions With Applicable
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Practice questions for this set


Learn 1 /7 Study using Learn




random, scale-free, small world, hub & spoke/star



Choose an answer



1 different kinds of networks 2 the law of the few



3 preferential attachment 4 twitter cascades example



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Terms in this set (102)



the law of the few a small number of influential people can generate a
groundswell of support for an idea, brand, or
phenomenon


two step flow model of influence a small group of influencers disseminates information
they get from mess media while modifying the
opinions of a large number of other people

, effect of social media on public companies can bypass media and go directly to
opinion influencers


accidental influencers examples brookline police department posting a police officer
delivering milk; charlie bit my finger


twitter cascades example dots by themselves, nobody reposts a tweet; one link
with many others branching from that, one person
posts a link and many repost it


twitter cascades definition an information diffusion process on twitter in which a
number of people make the same decision of
passing along information in a sequential fashion


factors that predict influence number of followers and past influence


degree distribution a few people have a huge number of connections
while most people have very few connections


different kinds of networks random, scale-free, small world, hub & spoke/star


induction phenomena/system --> data collection -->
abstraction/analysis/model --> hypothesis/stylized
facts --> theory --> phenomena/system


glass of red wine phenomena/system --> theory -->
hypothesis/stylized facts --> data collection -->
abstraction/analysis/model --> phenomena/system


random (erdos-renyi) start with n nodes, form independent links, givens
graphs/networks benchmarks; properties look for both numerical and
analytical solutions


scale-free networks class of networks whose degree distribution follows
a power law; as a result these networks include many
"very-connected" nodes known as the hubs of
connectivity responsible for shaping the way the
network operates

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