Temporal Sequence of Retweets Help to Detect Influential Nodes in Social Networks
(2019) IEEE Transactions on Computational Social Systems — Vol. 6, n° 3, p. 441-455 (2019)
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- Authors
Bhowmick, Ayan Kumar 
Lambiotte, Renaud
- Abstract
- Identification of influential users in online social networks allows to facilitate efficient information diffusion to a large part of the network and thus benefiting diverse applications including viral marketing, disease control, and news dissemination. Existing methods have mainly relied on the network structure only for the detection of influential users. In this paper, we enrich this approach by proposing a fast, efficient, and unsupervised algorithm SmartInf to detect a set of influential users by identifying anchor nodes from a temporal sequence of retweets in Twitter cascades. Such anchor nodes provide important signatures of tweet diffusion across multiple diffusion localities and, hence, act as precursors for detection of influential nodes. 1 The set of influential nodes identified by SmartInf has the capacity to expose the tweet to a large and diverse population, when targeted as seeds thereby maximizing the influence spread. Experimental evaluation on empirical datasets from Twitter shows the superiority of SmartInf over state-of-the-art baselines in terms of infecting larger population; further, our evaluation shows that SmartInf is scalable to large-scale networks and is robust to missing data. Finally, we investigate the key factors behind the improved performance of SmartInf by testing our algorithm on a synthetic network using synthetic cascades simulated on this network. Our results reveal the effectiveness of SmartInf in identifying a diverse set of influential users that facilitate faster diffusion of tweets to a larger population. 1 We use the terms “influential nodes” and “influential users” interchangeably.
- Affiliations
Citations
Bhowmick, A. K., GUEUNING, M., Delvenne, J.-C., Lambiotte, R., & Mitra, B. (2019). Temporal Sequence of Retweets Help to Detect Influential Nodes in Social Networks. IEEE Transactions on Computational Social Systems, 6(3), 441-455. https://doi.org/10.1109/tcss.2019.2907553 (Original work published 2019)