Simulating the merge between user-centered graphs of social networks

Amara, Amina; Romdhane, Rihem Ben; Taieb, Mohamed Ali Hadj; Aouicha, Mohamed Ben

doi:10.1109/aiccsa.2016.7945817

Cited by 3 publications

(2 citation statements)

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“…In this respect, social data have been leveraged by researchers on many emerging cross-network applications (Amara et al. , 2016, 2022; Zhang et al.…”

Section: Introductionmentioning

confidence: 99%

“…In this respect, social data have been leveraged by researchers on many emerging crossnetwork applications (Amara et al, 2016(Amara et al, , 2022Zhang et al, 2018). In fact, analyzing user's data collected from various SM websites together rather than limiting to one website leads to better understand user's personality and interests so as to build a rich and complete profile serving for solving "cold-start" problem and data sparsity in many predictive tasks (Deng et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Cross-social networks analysis: building me-edge centered BUNet dataset based on implicit bridge users

Amara

Aouicha

2022

OIR

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PurposeThe intensive blooming of social media, specifically social networks, pushed users to be integrated into more than one social network and therefore many new “cross-network” scenarios have emerged, including cross-social networks content posting and recommendation systems. For this reason, it is mightily a necessity to identify implicit bridge users across social networks, known as social network reconciliation problem, to deal with such scenarios.Design/methodology/approachWe propose the BUNet (Bridge Users for cross-social Networks analysis) dataset built on the basis of a feature-based approach for identifying implicit bridge users across two popular social networks: Facebook and Twitter. The proposed approach leverages various similarity measures for identity matching. The Jaccard index is selected as the similarity measure outperforming all the tested measures for computing the degree of similarity between friends’ sets of two accounts of the same real person on two different social networks. Using “cross-site” linking functionality, the dataset is enriched by explicit me-edges from other social media websites.FindingsUsing the proposed approach, 399,407 users are extracted from different social platforms including an important number of bridge users shared across those platforms. Experimental results demonstrate that the proposed approach achieves good performance on implicit bridge users’ detection.Originality/valueThis paper contributes to the current scarcity of literature regarding cross-social networks analysis by providing researchers with a huge dataset of bridge users shared between different types of social media platforms.

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“…In this respect, social data have been leveraged by researchers on many emerging cross-network applications (Amara et al. , 2016, 2022; Zhang et al.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%