On the effectiveness of random jumps in an influence maximization algorithm for unknown graphs

Mihara, Shodai; Tsugawa, Sho; Ohsaki, Hiroyuki

doi:10.1109/icoin.2017.7899466

Cited by 9 publications

(21 citation statements)

References 19 publications

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“…Particularly relevant to our present study are the recent works on influence maximization for unknown graphs [27,28,32,36,37]. Mihara et al [27] use a biased sampling strategy to greedily probe and seed nodes with the highest expected degree, given the activated nodes from the previous step.…”

Section: Related Workmentioning

confidence: 99%

“…Mihara et al [27] use a biased sampling strategy to greedily probe and seed nodes with the highest expected degree, given the activated nodes from the previous step. They later propose to include random flights to improve this heuristic [28]. Stein et al [32] explore applications of common heuristics and other known algorithms in scenarios where parts of the network is completely unobservable.…”

Section: Related Workmentioning

confidence: 99%

“…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.…”

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confidence: 99%

“…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%

“…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.…”

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confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Seeding with Costly Network Information

et al. 2019

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Robustness of Influence Maximization Against Non-adversarial Perturbations

Tsugawa

Ohsaki

2018

Lecture Notes in Social Networks

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Influence maximization under limited network information: seeding high-degree neighbors

Buskens²,

Rijt³

et al. 2022

J. Phys. Complex.

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The diffusion of information, norms, and practices across a social network can be initiated by compelling a small number of seed individuals to adopt first. Strategies proposed in previous work either assume full network information or a large degree of control over what information is collected. However, privacy settings on the Internet and high non-response in surveys often severely limit available connectivity information. Here we propose a seeding strategy for scenarios with limited network information: Only the degrees and connections of some random nodes are known. This new strategy is a modification of ``random neighbor sampling'' (or "one-hop") and seeds the highest-degree neighbors of randomly selected nodes. Simulating a fractional threshold model, we find that this new strategy excels in networks with heavy tailed degree distributions such as scale-free networks and large online social networks. It outperforms the conventional one-hop strategy even though the latter can seed 50% more nodes, and other seeding possibilities including purely high-degree seeding and clustered seeding.

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On the effectiveness of random jumps in an influence maximization algorithm for unknown graphs

Cited by 9 publications

References 19 publications

Seeding with Costly Network Information

Seeding with Costly Network Information

Robustness of Influence Maximization Against Non-adversarial Perturbations

Influence maximization under limited network information: seeding high-degree neighbors

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