Using Link Clustering to Detect Influential Spreaders

Krukowski, Simon; Hecking, Tobias

doi:10.1007/978-3-030-65347-7_34

Cited by 1 publication

(8 citation statements)

References 22 publications

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“…In our recent publication (Krukowski and Hecking 2021 ), we could show that the above hypothesised relationship between the community membership of nodes and their spreading capability does exist, and that nodes, which are members of multiple overlapping sub-communities, do indeed prove to be influential spreaders. Details about the utilisation of the algorithm for detecting influential spreaders will be given in the “ Estimating spreading capability using global information ” section.…”

Section: Introductionmentioning

confidence: 82%

“…In this paper, we intended to complement recent findings (Krukowski and Hecking 2021 ) regarding spreading processes within complex networks and the immunisation of nodes using topological features, specifically the community structure. In doing so, we approach two goals: Firstly, extend our understanding of our recently introduced measure of community centrality ( ), and secondly, develop a new measure to predict the efficiency of the spreading process which can be used without knowledge of the global network structure ( ).…”

Section: Discussionmentioning

confidence: 99%

“…While for , the community centrality is calculated using link clustering (Ahn et al. 2010 ; Krukowski and Hecking 2021 ), for the , an induced subgraph for every node of the network is created (i.e., ego-network), which is then clustered using the Louvain-method (Blondel et al. 2008 ).…”

Section: Discussionmentioning

confidence: 99%

“…While there are good indicators that the k -Shell index of nodes or link communities work well for identifying influential spreaders in networks (Krukowski and Hecking 2021 ; Kitsak et al. 2010 ), the drawback of the required knowledge about the global network structure warrants strategies which allow for an immunisation of relevant nodes irrespective of such knowledge.…”

Section: Methodsmentioning

confidence: 99%

“…nodes with the highest degree), but the ones located in densely connected cores of the network indicated by a high k-shell index. This insight was accompanied by our recent study on the positive effect of node membership in multiple overlapping and densely connected clusters in a network (Krukowski and Hecking 2021). Here, the general idea is that someone who is member of many different overlapping social groups (workplace, sports club, friendship circles) is better capable of injecting a spreading item into various densely connected regions of the network where it further circulates.…”

Section: Introductionmentioning

confidence: 94%

See 4 more Smart Citations

Global and local community memberships for estimating spreading capability of nodes in social networks

Krukowski

Hecking

2021

Appl Netw Sci

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The analysis of spreading processes within complex networks can offer many important insights for the application in contexts such as epidemics, information dissemination or rumours. Particularly, structural factors of the network which either contribute or hinder the spreading are of interest, as they can be used to control or predict such processes. In social networks, the community structure is especially relevant, as actors usually participate in different densely connected social groups which emerge from various contexts, potentially allowing them to inject the spreading process into many different communities quickly. This paper extends our recent findings on the community membership of nodes and how it can be used to predict their individual spreading capability (Krukowski and Hecking, in: Benito, Cherifi, Cherifi, Moro, Rocha, Sales-Pardo (eds) Complex networks & their applications IX. Springer, Cham, pp 408–419, 2021) by further evaluating it on additional networks (both real-world networks and artificially generated networks), while additionally introducing a new local measure to identify influential spreaders that—in contrast to most other measures, does not rely on knowledge of the global network structure. The results confirm our recent findings, showing that the community membership of nodes can be used as a predictor for their spreading capability, while also showing that especially the local measure proves to be a good predictor, effectively outperforming the global measure in many cases. The results are discussed with regard to real-world use cases, where knowledge of the global structure is often not given, yet a prediction regarding the spreading capability highly desired (e.g., contact-tracing apps).

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Section: Introductionmentioning

confidence: 82%