Influence Maximization (IM), which selects a set of $k$ users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technical challenges, IM has been extensively studied in the past decade. In this paper, we survey and synthesize a wide spectrum of existing studies on IM from an algorithmic perspective , with a special focus on the following key aspects: (1) a review of well-accepted diffusion models that capture the information diffusion process and build the foundation of the IM problem, (2) a fine-grained taxonomy to classify existing IM algorithms based on their design objectives, (3) a rigorous theoretical comparison of existing IM algorithms, and (4) a comprehensive study on the applications of IM techniques in combining with novel context features of social networks such as topic, location, and time. Based on this analysis, we then outline the key challenges and research directions to expand the boundary of IM research.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4983&context=sis_research

Influence Maximization on Social Graphs: A Survey

Given a social network with diffusion probabilities as edge weights and an integer k, which k nodes should be chosen for initial injection of information to maximize influence in the network? This problem is known as Target Set Selection in a social network (TSS Problem) and more popularly, Social Influence Maximization Problem (SIM Problem). This is an active area of research in computational social network analysis domain since one and half decades or so. Due to its practical importance in various domains, such as viral marketing, target advertisement, personalized recommendation, the problem has been studied in different variants, and different solution methodologies have been proposed over the years. Hence, there is a need for an organized and comprehensive review on this topic. This paper presents a survey on the progress in and around TSS Problem. At last, it discusses current research trends and future research directions as well.

A Survey on Influence Maximization in a Social Network

Identification of influential users for news and messages propagation constitutes one of the most important topics in analysis of social networks. The success of the spreading process in these networks depends on the mechanism for selection and specification of the influential users. Beside selection of influential users, the distances between selected users should be considered in this mechanism to ensure minimum overlap and maximum coverage of a wider area of the network. Simultaneous meeting of the two objectives may be contradictory. In this paper, we propose a new approach for selection of the set of influential users using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method that seeks to present a solution by considering the above two objectives. The simulation results over real-world and artificial networks demonstrate that the set selected by the proposed approach exhibits greater spread of influence than those selected by the traditional approaches.

Influence maximization in social networks based on TOPSIS

TIFIM: A Two-stage Iterative Framework for Influence Maximization in Social Networks

Information propagation in online social networks (OSNs), which helps shaping consumers' purchasing decisions, has received a lot of attention. The ultimate goal of marketing and advertising in OSNs is to massively influence audiences and enlarge the number of product adoptions. Most of existing works focus on maximizing the influence of a single product or promoting the adoption of one product in competing campaigns. However, in reality, the majority of companies produce various products for supplying customers with different needs. Therefore, it is truly significant and also challenging to wisely distribute limited budget across multiple products in viral marketing. In this paper, we investigate a Profit Maximization with Multiple Adoptions (PM2A) problem, which aims at maximizing the overall profit across all products. The natural greedy fails to provide a bounded result. In order to select high quality seeds for information propagation, we first proposed the PMCE algorithm, which has a ratio 1/2 (1 − 1/e2). Moreover, we further improve this ratio to (1−1/e) by proposing the PMIS algorithm. Comprehensive experiments on three real social networks are conducted. And results show that our algorithms outperform other heuristics, and better distribute the budget in terms of profit maximization.

Profit maximization for multiple products in online social networks

1.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Social Influence And Influence Maximization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Information Diffusion Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.1 Threshold Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4.1.1 Linear Threshold Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.4.1.2 The Majority Threshold Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.4.1.3 The Small Threshold Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.4.1.4 The Unanimous Threshold Model . . . . . . . . . . . . . . . . . . . . . . . . . 10 Other Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.4.2 Cascading Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

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Huiyuan Zhang

Papers

Recent Advances in Information Diffusion and Influence Maximization of Complex Social Networks