A holistic approach to influence maximization in social networks: STORIE

• Published in: Applied soft computing Vol. 66; pp. 533 - 547
• Main Authors: N., Sumith, B., Annappa, Bhattacharya, Swapan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2018
• Subjects: Crowdsourcing; Estimation; Heuristics; Influence maximization; Pruning; Small-world properties; Social networks; Viral marketing; Crowdsourcing; Influence maximization; Estimation; Heuristics; Viral marketing; Pruning; Social networks; Small-world properties
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.12.025

•STORIE bridges the gap between theoretical formulation and real world applicability.•It merges aspects of network structure, parameter estimation and heuristics.•The RnSIR model depicts the user's role in the diffusion process in social networks.•The scalability issue, estimating influence and fetching top influential users is addressed. [Display omitted] Crowd sourcing techniques are used in social networks to propagate information at a faster pace through campaigns. One of the challenges of crowd sourcing system is to recruit right users to be a part of successful campaigns. Fetching this right group of people, who influence a vast population to adopt information, is termed as influence maximization. Concerns of scalability and effectiveness need an effective and a viable solution. This paper proposes the solution in three stages. At the first stage, the large social network is pruned based on the nodal properties to make the solution scalable. At the second stage, Outdegree Rank (OR), is proposed and at the third stage, Influence Estimation (IE) approach estimates user influence. This work amalgamates aspects of structure, heuristic and user influence, to form STORIE. The proposed approach is compared to standard heuristics, on various experimental setups such as RNNDp, RNUDp and TVM. The spread of information is observed for HEP, PHY, Twitter, Infectious and YouTube data, under Independent Cascade model and STORIE gives optimal results, with an increase up to 50%. Although the paper discusses influence maximization, the proposed approach is also applicable to understand the spread of epidemics, computer virus, and rumor spreading in the real world and can also be extended to detect anomalies in web and social networks.