Influencers identification in complex networks through reaction-diffusion dynamics

Iannelli, Flavio; Sokolov, I. M.

doi:10.1103/physreve.98.062302

Cited by 20 publications

(29 citation statements)

References 78 publications

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“…The k-core's performance is maximal or nearly maximal for λ ≥ 5 λ c ; for both Cond-mat and Facebook, the closeness centrality performs optimally or nearly optimally for λ ≥ 5, which supports the competitiveness of distance-based centrality metrics for processes that are sufficiently far from the critical point [19].…”

Section: Identification Of Late-time Influencers Through Centrality Mmentioning

confidence: 62%

“…Given a network, the late-time influence of the nodes depends on the target dynamics under consideration. In line with a popular stream of physics literature [9,11,15,16,19,20,32], we focus here on the Susceptible-Infected-Recovered dynamics (SIR). The SIR dynamics on a given network starts from an initial configuration where some of the nodes (typically referred to as "seed nodes") are in the infected (I) state.…”

Section: The Traditional Definition Of Influencers Based On Spreadingmentioning

confidence: 99%

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Fast influencers in complex networks

Fang

Lü

Mariani

2019

Communications in Nonlinear Science and Numerical Simulation

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Influential nodes in complex networks are typically defined as those nodes that maximize the asymptotic reach of a spreading process of interest. However, for practical applications such as viral marketing and online information spreading, one is often interested in maximizing the reach of the process in a short amount of time. The traditional definition of influencers in network-related studies from diverse research fields narrows down the focus to the late-time state of the spreading processes, leaving the following question unsolved: which nodes are able to initiate large-scale spreading processes, in a limited amount of time? Here, we find that there is a fundamental difference between the nodes -which we call "fast influencers" -that initiate the largest-reach processes in a short amount of time, and the traditional, "late-time" influencers. Stimulated by this observation, we provide an extensive benchmarking of centrality metrics with respect to their ability to identify both the fast and late-time influencers. We find that local network properties can be used to uncover the fast influencers. In particular, a parsimonious, local centrality metric (which we call social capital) achieves optimal or nearly-optimal performance in the fast influencer identification for all the analyzed empirical networks. Local metrics tend to be also competitive in the traditional, late-time influencer identification task.

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Section: Identification Of Late-time Influencers Through Centrality Mmentioning

confidence: 62%

Section: The Traditional Definition Of Influencers Based On Spreadingmentioning

confidence: 99%

Fast influencers in complex networks

Fang

Lü

Mariani

2019

Communications in Nonlinear Science and Numerical Simulation

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“…The average distance of a node from the other nodes in the network can be interpreted as its centrality. Different choices of the distance function lead to different centrality metrics: for example, the shortest-path distance leads to the classical closeness centrality, whereas the randomwalk effective distance leads to the ViralRank centrality [29]. Another classical algorithm is the betweenness centrality and its variants, which build on the assumption that a given node i is central if many shortest paths that connect pairs of nodes pass through node i [30].…”

Section: Network-based Ranking Algorithmsmentioning

confidence: 99%

“…However, PageRank performs poorly in other problems. Other centrality metrics-like the degree centrality, the k-core centrality [51], and ViralRank [29]-substantially outperform PageRank in finding the most influential spreaders in a network [29]. Building on optimal percolation theory, the collective influence metric significantly outperforms PageRank in detecting the structural influencers [23].…”

Section: Performance Variabilitymentioning

confidence: 99%

Network-based ranking in social systems: three challenges

Lü¹

2020

J. Phys. Complex.

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Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (1) rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents' decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.

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“…Lots of centrality measures have been proposed to identify these nodes with huge influence in the complex network [20], the number of vital nodes is very small, but the impact would be indeed much larger than the other nodes. The classical centrality measures contain Betweenness Centrality [21], Closeness Centrality [22], Degree Centrality [23], PageRank [24], and lots of other measures [25].…”

Section: Introductionmentioning

confidence: 99%

Vital spreaders identification in complex networks with multi-local dimension

Wen

Pelusi

Deng

2020

Knowledge-Based Systems

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The important nodes identification has been an interesting problem in this issue. Several centrality measures have been proposed to solve this problem, but most of previous methods have their own limitations. To address this problem more effectively, multi-local dimension (MLD) which is based on the fractal property is proposed to identify the vital spreaders in this paper. This proposed method considers the information contained in the box and q plays a weighting coefficient for this partition information. MLD would have different expressions with different value of q, and it would degenerate to local information dimension and variant of local dimension when q = 1 when q = 0 respectively, both of which have been effective identification method for influential nodes. Thus, MLD would be a more general method which can degenerate to some exiting centrality measures. In addition, different with classical methods, the node with low MLD would be more important in the network. Some comparison methods and real-world complex networks are applied in this paper to show the effectiveness and reasonableness of this proposed method. The experiment results show the superiority of this proposed method.

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Influencers identification in complex networks through reaction-diffusion dynamics

Cited by 20 publications

References 78 publications

Fast influencers in complex networks

Fast influencers in complex networks

Network-based ranking in social systems: three challenges

Vital spreaders identification in complex networks with multi-local dimension

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