Deterministic graph exploration for efficient graph sampling

Salamanos, Nikos; Voudigari, Elli; Yannakoudakis, E. J.

doi:10.1007/s13278-017-0441-6

Cited by 20 publications

(16 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A detailed analysis of the algorithm can be found in Voudigari et al (2016) and Salamanos et al (2017) , where we have thoroughly studied the properties and the efficiency of the algorithm as well as other variations of the selection rule.…”

Section: The Rank Degree Methodsmentioning

confidence: 99%

“…A first preliminary study which investigates the applications of graph sampling to the influential spreaders identification problem has been conducted in Salamanos et al (2016) , where we studied the effectiveness of Rank Degree as influential spreaders identifier. The Rank Degree is a graph exploration sampling method which can produce representative samples/subgraphs from an unknown graph, using only local information, that is the degree of the visited nodes ( Voudigari et al 2016 ; Salamanos et al 2017 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A graph exploration method for identifying influential spreaders in complex networks

2017

Self Cite

View full text Add to dashboard Cite

The problem of identifying the influential spreaders - the important nodes - in a real world network is of high importance due to its theoretical interest as well as its practical applications, such as the acceleration of information diffusion, the control of the spread of a disease and the improvement of the resilience of networks to external attacks. In this paper, we propose a graph exploration sampling method that accurately identifies the influential spreaders in a complex network, without any prior knowledge of the original graph, apart from the collected samples/subgraphs. The method explores the graph, following a deterministic selection rule and outputs a graph sample - the set of edges that have been crossed. The proposed method is based on a version of Rank Degree graph sampling algorithm. We conduct extensive experiments in eight real world networks by simulating the susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) epidemic models which serve as ground truth identifiers of nodes spreading efficiency. Experimentally, we show that by exploring only the 20% of the network and using the degree centrality as well as the k-core measure, we are able to identify the influential spreaders with at least the same accuracy as in the full information case, namely, the case where we have access to the original graph and in that graph, we compute the centrality measures. Finally and more importantly, we present strong evidence that the degree centrality - the degree of nodes in the collected samples - is almost as accurate as the k-core values obtained from the original graph.

show abstract

Section: The Rank Degree Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A graph exploration method for identifying influential spreaders in complex networks

2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Borgatti et al [17] and Frantz et al [26] have investigated the consistency of node rankings between a ground-truth network and a network with errors. The robustness of influence measures against node sampling has been also investigated [3,19,24]. Salamanos et al [24] have addressed the problem of finding the top-k central nodes in a ground-truth social network from a sampled social network.…”

Section: Related Workmentioning

confidence: 99%

“…The robustness of influence measures against node sampling has been also investigated [3,19,24]. Salamanos et al [24] have addressed the problem of finding the top-k central nodes in a ground-truth social network from a sampled social network. These studies address the problem of identifying the top-ranked nodes on the basis of influence measures in a ground-truth social network from an incomplete social network.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Identifying influencers from sampled social networks

Tsugawa

Kimura

2018

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

Identifying influencers who can spread information to many other individuals from a social network is a fundamental research task in the network science research field. Several measures for identifying influencers have been proposed, and the effectiveness of these influence measures has been evaluated for the case where the complete social network structure is known. However, it is difficult in practice to obtain the complete structure of a social network because of missing data, false data, or node/link sampling from the social network. In this paper, we investigate the effects of node sampling from a social network on the effectiveness of influence measures at identifying influencers. Our experimental results show that the negative effect of biased sampling, such as sample edge count, on the identification of influencers is generally small. For social media networks, we can identify influencers whose influence is comparable with that of those identified from the complete social networks by sampling only 10-30% of the networks. Moreover, our results also suggest the possible benefit of network sampling in the identification of influencers. Our results show that, for some networks, nodes with higher influence can be discovered from sampled social networks than from complete social networks.

show abstract

An Overview on Reducing Social Networks’ Size

Jaouadi

Romdhane

2022

Advanced Data Mining and Applications

View full text Add to dashboard Cite

Deterministic graph exploration for efficient graph sampling

Cited by 20 publications

References 32 publications

A graph exploration method for identifying influential spreaders in complex networks

A graph exploration method for identifying influential spreaders in complex networks

Identifying influencers from sampled social networks

An Overview on Reducing Social Networks’ Size

Contact Info

Product

Resources

About