[link] Large Social Networks can be Targeted for Viral Marketing with Small Seed Sets

Gunnar_Zarncke

Large Social Networks can be Targeted for Viral Marketing with Small Seed Sets

It shows how easy a population can be influenced if control over a small sub-set exists.

A key problem for viral marketers is to determine an initial "seed" set [<1% of total size] in a network such that if given a property then the entire network adopts the behavior. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real-world networks, we found that our approach often finds such sets that are several orders of magnitude smaller than the population size. Our approach also scales well - on a Friendster social network consisting of 5.6 million nodes and 28 million edges we found a seed sets in under 3.6 hours. We also find that highly clustered local neighborhoods and dense network-wide community structure together suppress the ability of a trend to spread under the tipping model.

This is relevant for LW because

a) Rational agents should hedge against this.

b) An UFAI could exploit this.

c) It gives hints to proof systems against this 'exploit'.

Large Social Networks can be Targeted for Viral Marketing with Small Seed Sets

It shows how easy a population can be influenced if control over a small sub-set exists.

A key problem for viral marketers is to determine an initial "seed" set [<1% of total size] in a network such that if given a property then the entire network adopts the behavior. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real-world networks, we found that our approach often finds such sets that are several orders of magnitude smaller than the population size. Our approach also scales well - on a Friendster social network consisting of 5.6 million nodes and 28 million edges we found a seed sets in under 3.6 hours. We also find that highly clustered local neighborhoods and dense network-wide community structure together suppress the ability of a trend to spread under the tipping model.

This is relevant for LW because

a) Rational agents should hedge against this.

b) An UFAI could exploit this.

c) It gives hints to proof systems against this 'exploit'.

True, but if marketing is your aim you are probably better off familiarizing yourself with standard best practices than with cutting-edge research.

Wouldn't a lot of either standard best practices or cutting edge research fall under what is considered "dark arts" here? So this really becomes "you can use Dark Arts for good purposes". Whether you should do so seems at least questionable, although I can see some arguments for it (such as when you need to use it to get rid of the effect of biases or to oppose someone else's dark arts).

1[anonymous]12y

There's a happy medium... a lot of the standard best practices become white noise because people see them all the time.

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[link] Large Social Networks can be Targeted for Viral Marketing with Small Seed Sets

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[link] Large Social Networks can be Targeted for Viral Marketing with Small Seed Sets

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