Document Type


Publication Date

January 2019


Social networking is an inevitable behavior of humans living in a society. In recent years, and with the rise of online social networks, personalized recommendations that leverage the social aspect have become a very intriguing domain for researchers. In this work, we explore how influence propagation and the decay in the cascading effect of influence from influential users can be leveraged to generate social graph-based recommendations. Understanding how influence propagates within a social network is itself a challenging problem. Few researchers have considered influence propagation and even fewer have considered decay in the cascading effect of influence in a social network. In this work we model the decay in influence propagation in directed graphs, utilizing the structural properties of the social graph to measure the propagated influence beyond one-hop. We then employ this influence propagation model to form social recommendations, and present our experimental results using real-life datasets.


This accepted manuscript/post-print originally appeared in: Gulati, A. and Eirinaki, M. (2019). Influence Propagation for Social Graph-based Recommendations. 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, pp. 2180-2189. doi: 10.1109/BigData.2018.8622213
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