Link Recommendation for Social Influence Maximization

2021 ◽  
Vol 15 (6) ◽  
pp. 1-23
Author(s):  
Federico Coró ◽  
Gianlorenzo D’angelo ◽  
Yllka Velaj
Keyword(s):  
Social Influence ◽  
User Behavior ◽  
Threshold Model ◽  
Fixed Number ◽  
Computational Time ◽  
Social Bias ◽  
Linear Threshold ◽  
The Social ◽  
Social Link ◽  
Link Recommendation

Social link recommendation systems, like “People-you-may-know” on Facebook, “Who-to-follow” on Twitter, and “Suggested-Accounts” on Instagram assist the users of a social network in establishing new connections with other users. While these systems are becoming more and more important in the growth of social media, they tend to increase the popularity of users that are already popular. Indeed, since link recommenders aim to predict user behavior, they accelerate the creation of links that are likely to be created in the future and, consequently, reinforce social bias by suggesting few (popular) users, giving few chances to most users to create new connections and increase their popularity. In this article, we measure the popularity of a user by means of her social influence, which is her capability to influence other users’ opinions, and we propose a link recommendation algorithm that evaluates the links to suggest according to their increment in social influence instead of their likelihood of being created. In detail, we give a factor approximation algorithm for the problem of maximizing the social influence of a given set of target users by suggesting a fixed number of new connections considering the Linear Threshold model as model for diffusion. We experimentally show that, with few new links and small computational time, our algorithm is able to increase by far the social influence of the target users. We compare our algorithm with several baselines and show that it is the most effective one in terms of increased influence.

scholarly journals Recommending Links to Maximize the Influence in Social Networks

2019 ◽  
Author(s):  
Federico Corò ◽  
Gianlorenzo D'Angelo ◽  
Yllka Velaj
Keyword(s):  
Social Media ◽  
Social Influence ◽  
Fixed Number ◽  
Constant Factor ◽  
Computational Time ◽  
Recommendation Algorithm ◽  
The Social ◽  
Social Link ◽  
Social Biases ◽  
Link Recommendation

Social link recommendation systems, like "People-you-may-know" on Facebook, "Who-to-follow" on Twitter, and "Suggested-Accounts" on Instagram assist the users of a social network in establishing new connections with other users. While these systems are becoming more and more important in the growth of social media, they tend to increase the popularity of users that are already popular. Indeed, since link recommenders aim at predicting users' behavior, they accelerate the creation of links that are likely to be created in the future, and, as a consequence, they reinforce social biases by suggesting few (popular) users, while giving few chances to the majority of users to build new connections and increase their popularity.In this paper we measure the popularity of a user by means of its social influence, which is its capability to influence other users' opinions, and we propose a link recommendation algorithm that evaluates the links to suggest according to their increment in social influence instead of their likelihood of being created. In detail, we give a constant factor approximation algorithm for the problem of maximizing the social influence of a given set of target users by suggesting a fixed number of new connections. We experimentally show that, with few new links and small computational time, our algorithm is able to increase by far the social influence of the target users. We compare our algorithm with several baselines and show that it is the most effective one in terms of increased influence.

scholarly journals Multi-Winner Election Control via Social Influence: Hardness and Algorithms for Restricted Cases

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 251
Author(s):  
Mohammad Abouei Mehrizi ◽  
Gianlorenzo D'Angelo
Keyword(s):  
Social Influence ◽  
Threshold Model ◽  
Dynamic Programming Algorithm ◽  
Political Campaigns ◽  
Programming Algorithm ◽  
Linear Threshold ◽  
Independent Cascade Model ◽  
Voting System ◽  
Independent Cascade ◽  
Party Voting

Nowadays, many political campaigns are using social influence in order to convince voters to support/oppose a specific candidate/party. In election control via social influence problem, an attacker tries to find a set of limited influencers to start disseminating a political message in a social network of voters. A voter will change his opinion when he receives and accepts the message. In constructive case, the goal is to maximize the number of votes/winners of a target candidate/party, while in destructive case, the attacker tries to minimize them. Recent works considered the problem in different models and presented some hardness and approximation results. In this work, we consider multi-winner election control through social influence on different graph structures and diffusion models, and our goal is to maximize/minimize the number of winners in our target party. We show that the problem is hard to approximate when voters’ connections form a graph, and the diffusion model is the linear threshold model. We also prove the same result considering an arborescence under independent cascade model. Moreover, we present a dynamic programming algorithm for the cases that the voting system is a variation of straight-party voting, and voters form a tree.

Optimal multi-community network modularity for information diffusion

2016 ◽  
Vol 27 (08) ◽  
pp. 1650092 ◽  
Author(s):  
Jiaocan Wu ◽  
Ruping Du ◽  
YingYing Zheng ◽  
Dong Liu
Keyword(s):  
Community Structure ◽  
Information Diffusion ◽  
Threshold Model ◽  
Social Reinforcement ◽  
Community Network ◽  
Linear Threshold ◽  
The Social ◽  
Network Modularity ◽  
Linear Threshold Model ◽  
The Impact

Studies demonstrate that community structure plays an important role in information spreading recently. In this paper, we investigate the impact of multi-community structure on information diffusion with linear threshold model. We utilize extended GN network that contains four communities and analyze dynamic behaviors of information that spreads on it. And we discover the optimal multi-community network modularity for information diffusion based on the social reinforcement. Results show that, within the appropriate range, multi-community structure will facilitate information diffusion instead of hindering it, which accords with the results derived from two-community network.

User Engagement in Feedback Sharing through Social Influence

2015 ◽  
pp. 234-257 ◽  
Author(s):  
Agnis Stibe ◽  
Harri Oinas-Kukkonen
Keyword(s):  
Information System ◽  
Social Influence ◽  
User Behavior ◽  
Social Influences ◽  
Design Principles ◽  
User Engagement ◽  
Experimental Setting ◽  
The Social ◽  
Technical Systems ◽  
Persuasive Systems

Organizations continuously strive to engage customers in the services development process. The Social Web facilitates this process by enabling novel channels for voluntary feedback sharing through social media and technologically advanced environments. This chapter explores how social influence design principles can enhance the effectiveness of socio-technical systems designed to alter human behavior with respect to sharing feedback. Drawing upon social science theories, this chapter develops a research framework that identifies social influence design principles pertinent to persuasive systems that facilitate user engagement in feedback sharing. The design principles are then implemented in an information system and their effects on feedback sharing are explored in an experimental setting. The main findings of this chapter contribute to research related to social influences on user behavior and to the practice of designing persuasive information systems.

scholarly journals Exploiting Social Influence to Control Elections Based on Scoring Rules

2019 ◽  
Author(s):  
Federico Corò ◽  
Emilio Cruciani ◽  
Gianlorenzo D'Angelo ◽  
Stefano Ponziani
Keyword(s):  
Social Influence ◽  
Control Problem ◽  
Threshold Model ◽  
Natural Extension ◽  
Borda Count ◽  
Voting Systems ◽  
Maximum Score ◽  
Influence Model ◽  
Linear Threshold ◽  
Linear Threshold Model

We consider the election control problem in social networks which consists in exploiting social influence in a network of voters to change their opinion about a target candidate with the aim of increasing his chances to win (constructive control) or lose (destructive control) the election. Previous works on this problem focus on plurality voting systems and on a influence model in which the opinion of the voters about the target candidate can only change by shifting its ranking by one position, regardless of the amount of influence that a voter receives. We introduce Linear Threshold Ranking, a natural extension of Linear Threshold Model, which models the change of opinions taking into account the amount of exercised influence. In this general model, we are able to approximate the maximum score that a target candidate can achieve up to a factor of 1-1/e by showing submodularity of the objective function. We exploit this result to provide a 1/3(1-1/e)-approximation algorithm for the constructive election control problem and a 1/2(1-1/e)-approximation ratio in the destructive scenario. The algorithm can be used in arbitrary scoring rule voting systems, including plurality rule and borda count.

Social Influence and the Social Self

1989 ◽  
Vol 34 (5) ◽  
pp. 450-451
Author(s):  
William P. Smith
Keyword(s):  
Social Influence ◽  
Social Self ◽  
The Social

scholarly journals What Drives Continuance Intention towards Social Media? Social Influence and Identity Perspectives

2020 ◽  
Vol 12 (17) ◽  
pp. 7081 ◽  
Author(s):  
Athapol Ruangkanjanases ◽  
Shu-Ling Hsu ◽  
Yenchun Jim Wu ◽  
Shih-Chih Chen ◽  
Jo-Yu Chang
Keyword(s):  
Social Media ◽  
Social Identity ◽  
New Media ◽  
Social Influence ◽  
Perceived Usefulness ◽  
Continuance Intention ◽  
Positive Effects ◽  
The Social ◽  
Media Platform ◽  
Continuous Usage Intention

With the growth of social media communities, people now use this new media to engage in many interrelated activities. As a result, social media communities have grown into popular and interactive platforms among users, consumers and enterprises. In the social media era of high competition, increasing continuance intention towards a specific social media platform could transfer extra benefits to such virtual groups. Based on the expectation-confirmation model (ECM), this research proposed a conceptual framework incorporating social influence and social identity as key determinants of social media continuous usage intention. The research findings of this study highlight that: (1) the social influence view of the group norms and image significantly affects social identity; (2) social identity significantly affects perceived usefulness and confirmation; (3) confirmation has a significant impact on perceived usefulness and satisfaction; (4) perceived usefulness and satisfaction have positive effects on usage continuance intention. The results of this study can serve as a guide to better understand the reasons for and implications of social media usage and adoption.

Pricing of Connected and Autonomous Vehicles in Mixed-Traffic Networks

2021 ◽  
pp. 036119812098585
Author(s):  
Paolo Delle Site
Keyword(s):  
Autonomous Vehicles ◽  
Marginal Cost ◽  
User Behavior ◽  
Social Planner ◽  
Traffic Networks ◽  
Mixed Traffic ◽  
The Third ◽  
First Case ◽  
Marginal Cost Pricing ◽  
The Social

For networks with human-driven vehicles (HDVs) only, pricing with arc-specific tolls has been proposed to achieve minimization of travel times in a decentralized way. However, the policy is hardly feasible from a technical viewpoint without connectivity. Therefore, for networks with mixed traffic of HDVs and connected and autonomous vehicles (CAVs), this paper considers pricing in a scenario where only CAVs are charged. In contrast to HDVs, CAVs can be managed as individual vehicles or as a fleet. In the latter case, CAVs can be routed to minimize the travel time of the fleet of CAVs or that of the entire fleet of HDVs and CAVs. We have a selfish user behavior in the first case, a private monopolist behavior in the second, a social planner behavior in the third. Pricing achieves in a decentralized way the social planner optimum. Tolls are not unique and can take both positive and negative values. Marginal cost pricing is one solution. The valid toll set is provided, and tolls are then computed according to two schemes: one with positive tolls only and minimum toll expenditure, and one with both tolls and subsidies and zero net expenditure. Convergent algorithms are used for the mixed-behavior equilibrium (simplicial decomposition algorithm) and toll determination (cutting plane algorithm). The computational experience with three networks: a two-arc network representative of the classic town bypass case, the Nguyen-Dupuis network, and the Anaheim network, provides useful policy insight.

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