Influence Maximization Problem for Unknown Social Networks

Mihara, Shodai; Tsugawa, Sho; Ohsaki, Hiroyuki

doi:10.1145/2808797.2808885

Cited by 43 publications

(45 citation statements)

References 23 publications

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“…This allows us to study the effect of varying the number of observations and provides a lower bound on the expected performance of PO-Place. With these settings, PO-Place closely resembles the algorithms presented by Borgs et al [1] and Mihara et al [15].…”

Section: Po-place Outputsupporting

confidence: 55%

“…As in this paper, Mihara et al [15] assume the network is initially unknown and show that influence maximisation effectiveness of 60-90% with 1-10% network observation is achievable. This work also uses a 'growing fringe' approach with priority based on degree estimation.…”

Section: Related Workmentioning

confidence: 92%

“…Many of the other approaches [1,15] to finding high-degree nodes select a random starting node and then 'grow' outwards, selecting the highest degree…”

Section: Partial Observability Algorithmmentioning

confidence: 99%

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Convention Emergence in Partially Observable Topologies

Marchant

Griffiths

2017

Autonomous Agents and Multiagent Systems

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Abstract. In multi-agent systems it is often desirable for agents to adhere to standards of behaviour that minimise clashes and wasting of (limited) resources. In situations where it is not possible or desirable to dictate these standards globally or via centralised control, convention emergence offers a lightweight and rapid alternative. Placing fixed strategy agents within a population, whose interactions are constrained by an underlying network, has been shown to facilitate faster convention emergence with some degree of control. Placing these fixed strategy agents at topologically influential locations (such as high-degree nodes) increases their effectiveness. However, finding such influential locations often assumes that the whole network is visible or that it is feasible to inspect the whole network in a computationally practical time, a fact not guaranteed in many real-world scenarios. We present an algorithm, PO-Place, that finds influential nodes given a finite number of network observations. We show that PO-Place finds sets of nodes with similar reach and influence to the set of high-degree nodes and we then compare the performance of PO-Place to degree placement for convention emergence in several real-world topologies.

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Section: Po-place Outputsupporting

confidence: 55%

Section: Related Workmentioning

confidence: 92%

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Convention Emergence in Partially Observable Topologies

Marchant

Griffiths

2017

Autonomous Agents and Multiagent Systems

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“…In [15], influence maximisation for unknown social networks is also investigated. Therein, a heuristic algorithm is proposed which works as follows: At every round, first a set of nodes are probed.…”

Section: Related Workmentioning

confidence: 99%

Influence maximisation beyond organisational boundaries

Stein

Eshghi

Bellamy

et al. 2017

2017 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computed, Scalable Computing &Amp; Commun

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Abstract-We consider the problem of choosing influential members within a social network, in order to disseminate a message as widely as possible. While this so-called problem of influence maximisation has been widely studied, little work considers partiallyobservable networks, where only part of a network is visible to the decision maker. Yet, this is critical in many applications, where an organisation needs to distribute its message far beyond its boundaries and beyond its usual sphere of influence. In this paper, we show that existing algorithms are not sufficient to handle such scenarios. To address this, we propose a set of novel adaptive algorithms that perform well in partially observable settings, achieving an up to 18% improvement on the non-adaptive state of the art.

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“…This is particularly important in online social networks with billions of edges, where working with the entire contacts lists might be impractical. Although the importance of influence maximization with partial network information has been observed and there are a few papers considering this problem, none of these previous work comes with provable quality guarantees for general graphs.In this work, we address the problem of influence maximization using partial information of the network which has attracted attention recently [27,28,32,36]. We enhance the theoretical foundations for what is achievable in this context by proposing a "probe-and-seed" algorithm that provides approximation guarantees for seeding with partial network information with a guaranteed upper bound on the number of edges queried.…”

mentioning

confidence: 99%

Seeding with Costly Network Information

et al. 2019

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The spread of behavior over social networks depends on the contact structure among individuals, and seeding the most influential agents can substantially enhance the extent of the spread. While the choice of the best seed set, known as influence maximization, is a computationally hard problem, many computationally efficient algorithms have been proposed to approximate the optimal seed sets with provable guarantees. Most of the previous work on influence maximization assumes the knowledge of the entire network graph. However, in practice, obtaining full knowledge of the network structure is very costly.In this work, we consider the choice of k initial seeds to maximize the expected number of adopters under the independent cascade model. We propose a "probe-and-seed" algorithm that provides almost tight approximation guarantees usingÕ(pn 2 + √ pn 1.5 ) edge queries inÕ(pn 2 + √ pn 1.5 ) time, where n is the network size and p is the probability of spreading through an edge. To the best of our knowledge, this is the first result to provide approximation guarantees for influence maximization, using a sub-quadratic number of queries for polynomially small p. We complement this result by showing that it is impossible to approximate the problem using o(n 2 ) edge queries for constant p. In the end, we consider a more advanced query model, where one seeds a node and observes the resultant adopters after running the spreading process. We provide an algorithm that uses onlyÕ(k 2 ) such queries and provides almost tight approximation guarantees.1 1 INTRODUCTION Decision-makers in marketing, public health, development, and other fields often have a limited budget for interventions, such that they can only target a small number of people for the intervention. Thus, in the presence of social or biological contagion, they strategize about where in a network to intervene -often where to seed a behavior (e.g., product adoption) by engaging in an intervention (e.g., giving a free product). The influence maximization literature is devoted to the study of algorithms for finding a set of k seeds so as to maximize the expected adoption, given a known network and a model of how individuals are affected by the intervention and others' adoptions [15]. However, finding the best k seeds for many models of social influence is NP-hard, as shown by Kempe, Kleinberg and Tardos [21]. Much of the subsequent influence maximization literature is concerned with developing efficient approximation algorithms with theoretical guarantees to make use of desirable properties of the influence function such as submodularity [10,34].With few exceptions [28,36], the seeding strategies that are studied in the influence maximization literature require explicit and complete knowledge of the network. However, collecting the entire network connection data can be difficult, costly, or impossible. For example, in development economics, public health, and education, data about network connections is often acquired through costly surveys (e.g., [4,7,8,30]). Indeed, t...

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Influence Maximization Problem for Unknown Social Networks

Cited by 43 publications

References 23 publications

Convention Emergence in Partially Observable Topologies

Convention Emergence in Partially Observable Topologies

Influence maximisation beyond organisational boundaries

Seeding with Costly Network Information

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