Identifying influencers in a social network : the value of real referral data

Individuals influence each other through social interactions and marketers aim to leverage this interpersonal influence to attract new customers. It still remains a challenge to identify those customers in a social network that have the most influence on their social connections. A common approach to the influence maximization problem is to simulate influence cascades through the network based on the existence of links in the network using diffusion models. Our study contributes to the literature by evaluating these principles using real-life referral behaviour data. A new ranking metric, called Referral Rank, is introduced that builds on the game theoretic concept of the Shapley value for assigning each individual in the network a value that reflects the likelihood of referring new customers. We also explore whether these methods can be further improved by looking beyond the one-hop neighbourhood of the influencers. Experiments on a large telecommunication data set and referral data set demonstrate that using traditional simulation based methods to identify influencers in a social network can lead to suboptimal decisions as the results overestimate actual referral cascades. We also find that looking at the influence of the two-hop neighbours of the customers improves the influence spread and product adoption. Our findings suggest that companies can take two actions to improve their decision support system for identifying influential customers: (1) improve the data by incorporating data that reflects the actual referral behaviour of the customers or (2) extend the method by looking at the influence of the connections in the two-hop neighbourhood of the customers

Towards Profit Maximization for Online Social Network Providers

Online Social Networks (OSNs) attract billions of users to share information and communicate where viral marketing has emerged as a new way to promote the sales of products. An OSN provider is often hired by an advertiser to conduct viral marketing campaigns. The OSN provider generates revenue from the commission paid by the advertiser which is determined by the spread of its product information. Meanwhile, to propagate influence, the activities performed by users such as viewing video ads normally induce diffusion cost to the OSN provider. In this paper, we aim to find a seed set to optimize a new profit metric that combines the benefit of influence spread with the cost of influence propagation for the OSN provider. Under many diffusion models, our profit metric is the difference between two submodular functions which is challenging to optimize as it is neither submodular nor monotone. We design a general two-phase framework to select seeds for profit maximization and develop several bounds to measure the quality of the seed set constructed. Experimental results with real OSN datasets show that our approach can achieve high approximation guarantees and significantly outperform the baseline algorithms, including state-of-the-art influence maximization algorithms.Comment: INFOCOM 2018 (Full version), 12 page

Gathering point-aided viral marketing in decentralized mobile social networks

Viral marketing is a technique that spreads advertisement information through social networks. Recently, viral marketing through online social networks has achieved huge commercial success. However, there are still very little research reported on viral marketing in decentralized mobile social networks (MSNs). Comparing with online viral marketing, viral marketing in decentralized MSNs faces many challenges, such as unreliable information diffusion and limited network knowledge. To address these problems, we propose the \textit{gathering point-aided mobile viral marketing (GP-MVM)} scheme, which contains two major components, i.e., \textit{seed selection} and \textit{information diffusion}. \textit{Seed selection} is responsible to select a set of seed nodes from which information diffusion begins. Based on a new metric called integrated contact strength (ICS), we propose two distributed seed selection schemes, i.e., \textit{ratio seeding} and \textit{threshold seeding}, while, for information diffusion, we propose the \textit{GP-aided diffusion} algorithm, which utilizes user GPs to promote information propagation. Continuous-time Markov chain-based analytical model shows that GP-MVM has a good scalability. Simulations indicate that GP-MVM outperforms two state-of-the-art information diffusion methods designed for MSNs, in terms of both diffusion proportion and diffusion speed