Social influence is a phenomenon describing the spread of opinions across the population. Nowadays, social influence analysis (SIA) has a great impact. For example, viral marketing and online content recommendation are applications of SIA. Hand-crafted features, as well as domain expert knowledge, are usually required in convention social influence analysis, but they incur high costs and are not scalable. Deep learning based approaches overcome these issues. For instance, a recently used approach learned latent features of users to predict social influence. In this paper, a teleport probability t from the page rank domain is integrated into the graph convolution network model for further enhance the performance of such an approach. In addition, a combined personalized propagation of neural predictions (CPPNP) algorithm leads to an impressive prediction accuracy when comparing with existing methods. Evaluation results on three well-known datasets reveal that optimizing t enhances the performance of CPPNP. Such a combined deep-learning and transfer-learning approach well supports the social influence prediction.

A combined deep-learning and transfer-learning approach for supporting social influence prediction / Cuzzocrea, A.; Leung, C. K.; Deng, D.; Mai, J. J.; Jiang, F.; Fadda, E.. - In: PROCEDIA COMPUTER SCIENCE. - ISSN 1877-0509. - 177:(2020), pp. 170-177. (Intervento presentato al convegno 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020 tenutosi a Madeira (Portugal) nel November 2-5, 2020) [10.1016/j.procs.2020.10.025].

A combined deep-learning and transfer-learning approach for supporting social influence prediction

Fadda E.
2020

Abstract

Social influence is a phenomenon describing the spread of opinions across the population. Nowadays, social influence analysis (SIA) has a great impact. For example, viral marketing and online content recommendation are applications of SIA. Hand-crafted features, as well as domain expert knowledge, are usually required in convention social influence analysis, but they incur high costs and are not scalable. Deep learning based approaches overcome these issues. For instance, a recently used approach learned latent features of users to predict social influence. In this paper, a teleport probability t from the page rank domain is integrated into the graph convolution network model for further enhance the performance of such an approach. In addition, a combined personalized propagation of neural predictions (CPPNP) algorithm leads to an impressive prediction accuracy when comparing with existing methods. Evaluation results on three well-known datasets reveal that optimizing t enhances the performance of CPPNP. Such a combined deep-learning and transfer-learning approach well supports the social influence prediction.
2020
File in questo prodotto:
File Dimensione Formato  
main.pdf

accesso aperto

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Creative commons
Dimensione 750.43 kB
Formato Adobe PDF
750.43 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2982932