A Link Strength Based Label Propagation Algorithm for Community Detection

Lakhdari, Abdallah; Chorana, Aicha; Cherroun, Hadda; Rezgui, Abdelmounaam

doi:10.1109/bdcloud-socialcom-sustaincom.2016.61

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…It accounts for heterogeneity in the node's degree of distribution and community size distribution. Community structure is one of the most important features of real social networks [13,34], where the LFR benchmark network reflects the real properties of nodes and communities [35].…”

Section: Before Diffusionmentioning

confidence: 99%

Combating fake news by empowering fact-checked news spread via topology-based interventions

Wang,

Yaqub,

Lakhdari

et al. 2021

Preprint

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Rapid information diffusion and large-scaled information cascades can enable the undesired spread of false information. A smallscaled false information outbreak may potentially lead to an infodemic. We propose a novel information diffusion and intervention technique to combat the spread of false news. As false information is often spreading faster in a social network, the proposed diffusion methodology inhibits the spread of false news by proactively diffusing the fact-checked information. Our methodology mainly relies on defining the potential super-spreaders in a social network based on their centrality metrics. We run an extensive set of experiments on different networks to investigate the impact of centrality metrics on the performance of the proposed diffusion and intervention models. The obtained results demonstrate that empowering the diffusion of fact-checked news combats the spread of false news further and deeper in social networks.CCS Concepts: • Security and privacy → Social aspects of security and privacy; • Human-centered computing → Social network analysis; Web-based interaction; Social content sharing; Computer supported cooperative work.

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Section: Before Diffusionmentioning

confidence: 99%

Combating fake news by empowering fact-checked news spread via topology-based interventions

Wang,

Yaqub,

Lakhdari

et al. 2021

Preprint

Self Cite

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“…Since it is developed by [2], the use of the algorithms is quite popular. The reason is mainly that of its use which does not require any parameters, easy implementation, fast execution for large networks, and the ability to detect valid communities in random graphs [15]. For that reason, studies using the algorithms continue, as conducted by [16]- [20].…”

Section: Label Propagationmentioning

confidence: 99%

Performance Analysis of Molecular Complex Detection in Social Network Datasets

Sulistianingsih¹,

Winarko²

2017

IJCA

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Researches related graph dataset conducted for years. One of its main topics was community detection. The development of algorithms to do community detection continuously conducted by adjusting characteristics of datasets used. One of which is Molecular Complex Detection (MCODE) algorithm used to community detection in a dataset of protein-protein interaction (PPI). However, use of the algorithm still limited to PPI dataset only. The aim of this research was to conducted experiment usage of MCODE algorithm in other datasets such us social network datasets. An experiment conducted by comparing the performance of MCODE with other benchmark algorithms such us Label Propagation and Girvan-Newman. From the experiment performed was resulted that for modularity MCODE showed the best result when compared with others, followed Girvan-Newman and Label Propagation with its values were 0.67, 0.66 and 0.46, respectively. Furthermore, for a testing parameter such us running time and average clustering coefficient, MCODE showed better result compared with Girvan-Newman and Label Propagation. For running time, MCODE needed mean time as 0.053 s, GirvanNewman as 0.056 s and Label Propagation as 0.078 s and for test parameter of average clustering coefficient, MCODE was 0.37, Girvan-Newman was 0.44 and Label Propagation was 0.46.

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