Ranking influential spreaders is an ill-defined problem

Gu, Jain; Lee, Sung-Min; Saramäki, Jari; Holme, Petter

doi:10.1209/0295-5075/118/68002

Cited by 14 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By an elaborately designed model, Gu et al. (2017) showed that there is no ground truth in ranking influential spreaders even with a given dynamics.…”

Section: Discussionmentioning

confidence: 99%

Progresses and challenges in link prediction

Zhou

2021

iScience

View full text Add to dashboard Cite

Summary Link prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of nonobserved links, based on known topology. After a brief introduction of the standard problem and evaluation metrics of link prediction, this review will summarize representative progresses about local similarity indices, link predictability, network embedding, matrix completion, ensemble learning, and some others, mainly extracted from related publications in the last decade. Finally, this review will outline some long-standing challenges for future studies.

show abstract

“…By an elaborately designed model, Gu et al. (2017) showed that there is no ground truth in ranking influential spreaders even with a given dynamics.…”

Section: Discussionmentioning

confidence: 99%

Progresses and challenges in link prediction

Zhou

2021

iScience

View full text Add to dashboard Cite

show abstract

“…Instead of actually running the spreading process, these metrics are mostly based on the local or global topology of a node in the network, for instance, number of immediate neighbors [28,29,59,157], global position [17,158,159,160,161,162], number of shortest paths [163,164,165,166], random walks [167,168,169], eigenvectors [170,171,172,173], path counting [174,175,176,177], etc. Even though the optimal metric that performs best for all spreading dynamics on all underlying networks does not seem to exist [178,179,180], these centrality-based approaches are still persistently used due to their simplicity and relative satisfactory performance in some occasions.…”

Section: Greedy Algorithmsmentioning

confidence: 99%

Influencer identification in dynamical complex systems

Pei

Wang

Morone

et al. 2019

Journal of Complex Networks

View full text Add to dashboard Cite

The integrity and functionality of many real-world complex systems hinge on a small set of pivotal nodes, or influencers. In different contexts, these influencers are defined as either structurally important nodes that maintain the connectivity of networks, or dynamically crucial units that can disproportionately impact certain dynamical processes. In practice, identification of the optimal set of influencers in a given system has profound implications in a variety of disciplines. In this review, we survey recent advances in the study of influencer identification developed from different perspectives, and present state-of-the-art solutions designed for different objectives. In particular, we first discuss the problem of finding the minimal number of nodes whose removal would breakdown the network (i.e., the optimal percolation or network dismantle problem), and then survey methods to locate the essential nodes that are capable of shaping global dynamics with either continuous (e.g., independent cascading models) or discontinuous phase transitions (e.g., threshold models). We conclude the review with a summary and an outlook.

show abstract

“…For vaccination, we will use the average outbreak size from one random seed node to estimate the importance of a node [3,8,15,22]. One could, optionally, rephrase it as a cost problem [12].…”

Section: A Importancementioning

confidence: 99%

“…We will, in general, call these sets active nodes and denote the number of them as n. We will try to find the optimal sets of active nodes (and call them optimal nodes). Note that this is not the same as ranking the nodes in order of importance and take the n most important ones-such a "greedy" approach can in many cases fail [8,14].…”

Section: A Importancementioning

confidence: 99%

Three faces of node importance in network epidemiology: Exact results for small graphs

Holme

2017

Phys. Rev. E

Self Cite

View full text Add to dashboard Cite

We investigate three aspects of the importance of nodes with respect to susceptible-infectious-removed (SIR) disease dynamics: influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size), and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all connected graphs of three to seven nodes. We obtain the smallest graphs where the optimal node sets are not overlapping. We find that (i) node separation is more important than centrality for more than one active node, (ii) vaccination and influence maximization are the most different aspects of importance, and (iii) the three aspects are more similar when the infection rate is low.

show abstract

Ranking influential spreaders is an ill-defined problem

Cited by 14 publications

References 26 publications

Progresses and challenges in link prediction

Progresses and challenges in link prediction

Influencer identification in dynamical complex systems

Three faces of node importance in network epidemiology: Exact results for small graphs

Contact Info

Product

Resources

About