Abstract
The propagation of concepts in a population of agents is a form of influence spread, which can be modelled as a cascade from a set of initially activated individuals. The study of such influence cascades, in particular the identification of influential individuals, has a wide range of applications including epidemic control, viral marketing and the study of social norms. In real-world environments there may be many concepts spreading and interacting. These interactions can affect the spread of a given concept, either boosting it and allowing it to spread further, or inhibiting it and limiting its capability to spread. Previous work does not consider how the interactions between concepts affect concept spread. Taking concept interactions into consideration allows for indirect concept manipulation, meaning that we can affect concepts we are not able to directly control. In this paper, we consider the problem of indirect concept manipulation, and propose heuristics for indirectly boosting or inhibiting concept spread in environments where concepts interact. We define a framework that allows for the interactions between any number of concepts to be represented, and present a heuristic that aims to identify important influence paths for a given target concept in order to manipulate its spread. We compare the performance of this heuristic, called maximum probable gain, against established heuristics for manipulating influence spread.
Highlights
In many environments it is possible for strategies, behaviours, knowledge or infections to spread within a population
We evaluate the performance of maximum probable gain (MPG) for both the Independent Cascade Model (ICM) and Linear Threshold Model (LTM) for the indirect influence maximisation problem, across three types of network namely small-world, scale-free and realworld
As with indirect concept maximisation, we evaluate the performance of MPG across a variety of network types
Summary
In many environments it is possible for strategies, behaviours, knowledge or infections to spread within a population. Populations of autonomous entities are complex systems, meaning that the net effects of propagation are hard to predict or influence, despite being due to individual behaviour. Such propagation is a form of influence spread, which can be modelled as a cascade from a set of initially activated individuals [1]. Insight gained from understanding how to manipulate cascades in abstract populations has many applications, such as informing epidemic control, viral marketing, and understanding convention emergence in multi-agent systems. Understanding how ideas propagate can inform viral marketing campaigns
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