Bridging the Gap Between Theory and Practice in Influence Maximization: Raising Awareness about HIV among Homeless Youth

Yadav, Amulya; Wilder, Bryan; Rice, Eric; Petering, Robin; Craddock, Jaih; Yoshioka‐Maxwell, Amanda; Hemler, Mary; Onasch‐Vera, Laura; Tambe, Milind; Woo, Darlene

doi:10.24963/ijcai.2018/761

Cited by 34 publications

(23 citation statements)

References 2 publications

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“…Although IM problem has been introduced in 2003 by Kempe et al and despite the fact that it has been proved that a greedy algorithm can solve it with a 1 − 1/e-optimally, a large literature exists in AI community on various approaches on making it more scalable [21], time-aware [13], cost-aware [23], topic-aware [2] and location-aware [18]. Also, besides the technical aspects of the IM problem, there have been efforts to address the challenges of deploying IM in real-world applications, e.g., healthcare [32]. However, there is a lack in the literature in addressing fairness-aware IM problems.…”

Section: Related Workmentioning

confidence: 99%

“…In fact, it was first introduced under the name of viral marketing and aimed at maximizing the profit of an advertiser who targets individuals in a social network [24]. In addition, IM has been used in various applications where the main focus is social good, such as HIV prevention for homeless youth [32] and financial inclusion [1]. Hence it is crucial to ensure that maximizing influence in a network is performed in a way that ensures a diverse spread of influence among various groups and communities.…”

Section: Introductionmentioning

confidence: 99%

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A Unifying Framework for Fairness-Aware Influence Maximization

Farnad

Babaki

Gendreau

2020

Companion Proceedings of the Web Conference 2020

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The problem of selecting a subset of nodes with greatest influence in a graph, commonly known as influence maximization, has been well studied over the past decade. This problem has real world applications which can potentially affect lives of individuals. Algorithmic decision making in such domains raises concerns about their societal implications. One of these concerns, which surprisingly has only received limited attention so far, is algorithmic bias and fairness. We propose a flexible framework that extends and unifies the existing works in fairness-aware influence maximization. This framework is based on an integer programming formulation of the influence maximization problem. The fairness requirements are enforced by adding linear constraints or modifying the objective function. Contrary to the previous work which designs specific algorithms for each variant, we develop a formalism which is general enough for specifying different notions of fairness. A problem defined in this formalism can be then solved using efficient mixed integer programming solvers. The experimental evaluation indicates that our framework not only is general but also is competitive with existing algorithms. CCS CONCEPTS• Social and professional topics → User characteristics; • Theory of computation → Mathematical optimization; • Networks → Online social networks.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Unifying Framework for Fairness-Aware Influence Maximization

Farnad

Babaki

Gendreau

2020

Companion Proceedings of the Web Conference 2020

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“…Matroids can capture a large number of influence limitation settings, especially when edges in the solution can be naturally divided into partitions. Examples include the limitation of influence in non-overlapping communities [3], disjoint campaigning [23], and problems where issues of fairness arise [44]. Moreover, influence boosting problems via attribute-level modification [25] and edge addition [19] can also be modelled under matroid constraints.…”

Section: Generalizationsmentioning

confidence: 99%

Influence Minimization Under Budget and Matroid Constraints: Extended Version

Medya,

Silva,

Singh

2019

Preprint

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Recently, online social networks have become major battlegrounds for political campaigns, viral marketing, and the dissemination of news. As a consequence, "bad actors" are increasingly exploiting these platforms, becoming a key challenge for their administrators, businesses and the society in general. The spread of fake news is a classical example of the abuse of social networks by these actors. While some have advocated for stricter policies to control the spread of misinformation in social networks, this often happens in detriment of their democratic and organic structure. In this paper we study how to limit the influence of a target set of users in a network via the removal of a few edges. The idea is to control the diffusion processes while minimizing the amount of disturbance in the network structure.We formulate the influence limitation problem in a data-driven fashion, by taking into account past propagation traces. Moreover, we consider two types of constraints over the set of edge removals, a budget constraint and also a, more general, set of matroid constraints. These problems lead to interesting challenges in terms of algorithm design. For instance, we are able to show that influence limitation is APX-hard and propose deterministic and probabilistic approximation algorithms for the budgeted and matroid version of the problem, respectively. Our experiments show that the proposed solutions outperform the baselines by up to 40%.

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“…Then, the challenge is to decide which people to "seed" with the product in order to maximize long-term adoption. Since its formulation by Domingos and Richardson, the influence maximization problem has found applications across diverse domains, from traditional applications in marketing (Hinz et al 2011), to the spreading of health information (Yadav et al 2018;Wilder et al 2018), to the diffusion of microfinance programs in villages (Banerjee et al 2013).…”

Section: Introductionmentioning

confidence: 99%

When can overambitious seeding cost you?

Iyer

Adamic

2019

Appl Netw Sci

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In the classic "influence-maximization" (IM) problem, people influence one another to adopt a product and the goal is to identify people to "seed" with the product so as to maximize long-term adoption. Many influence-maximization models suggest that, if the number of people who can be seeded is unconstrained, then it is optimal to seed everyone at the start of the IM process. In a recent paper, we argued that this is not necessarily the case for social products that people use to communicate with their friends (Iyer and Adamic, The costs of overambitious seeding of social products. In: International Workshop on Complex Networks and Their Applications_273-286, 2018). Through simulations of a model in which people repeatedly use such a product and update their rate of subsequent usage depending upon their satisfaction, we showed that overambitious seeding can result in people adopting in suboptimal contexts, having bad experiences, and abandoning the product before more favorable contexts for adoption arise. Here, we extend that earlier work by showing that the costs of overambitious seeding also appear in more traditional threshold models of collective behavior, once the possibility of permanent abandonment of the product is introduced. We further demonstrate that these costs can be mitigated by using conservative seeding approaches besides those that we explored in the earlier paper. Synthesizing these results with other recent work in this area, we identify general principles for when overambitious seeding can be of concern in the deployment of social products.

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Bridging the Gap Between Theory and Practice in Influence Maximization: Raising Awareness about HIV among Homeless Youth

Cited by 34 publications

References 2 publications

A Unifying Framework for Fairness-Aware Influence Maximization

A Unifying Framework for Fairness-Aware Influence Maximization

Influence Minimization Under Budget and Matroid Constraints: Extended Version

When can overambitious seeding cost you?

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