scholarly journals Cost-Effective Viral Marketing for Time-Critical Campaigns in Large-Scale Social Networks

2014 ◽  
Vol 22 (6) ◽  
pp. 2001-2011 ◽  
Author(s):  
Thang N. Dinh ◽  
Huiyuan Zhang ◽  
Dzung T. Nguyen ◽  
My T. Thai
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Cost Effective ◽  
Viral Marketing ◽  
Time Critical

A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks

2013 ◽  
Vol 9 (1) ◽  
pp. 36-53
Author(s):  
Evis Trandafili ◽  
Marenglen Biba
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Networks ◽  
Large Scale ◽  
Viral Marketing ◽  
Social Network Mining ◽  
Business Applications ◽  
Mining Community ◽  
Recent Developments ◽  
Analysis Of Social Networks

Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution of such networks has posed outstanding challenges for the learning and mining community, and on the other has opened the possibility for very powerful business applications. However, little understanding exists regarding these business applications and the potential of social network mining to boost marketing. This paper presents a review of the most important state-of-the-art approaches in the machine learning and data mining community regarding analysis of social networks and their business applications. The authors review the problems related to social networks and describe the recent developments in the area discussing important achievements in the analysis of social networks and outlining future work. The focus of the review in not only on the technical aspects of the learning and mining approaches applied to social networks but also on the business potentials of such methods.

scholarly journals Local structure can identify and quantify influential global spreaders in large scale social networks

2018 ◽  
Vol 115 (29) ◽  
pp. 7468-7472 ◽  
Author(s):  
Yanqing Hu ◽  
Shenggong Ji ◽  
Yuliang Jin ◽  
Ling Feng ◽  
H. Eugene Stanley ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Large Scale ◽  
Characteristic Number ◽  
Percolation Cluster ◽  
Network Size ◽  
Viral Marketing ◽  
Nucleation Behavior ◽  
Practical Applications ◽  
Entire Network

Measuring and optimizing the influence of nodes in big-data online social networks are important for many practical applications, such as the viral marketing and the adoption of new products. As the viral spreading on a social network is a global process, it is commonly believed that measuring the influence of nodes inevitably requires the knowledge of the entire network. Using percolation theory, we show that the spreading process displays a nucleation behavior: Once a piece of information spreads from the seeds to more than a small characteristic number of nodes, it reaches a point of no return and will quickly reach the percolation cluster, regardless of the entire network structure; otherwise the spreading will be contained locally. Thus, we find that, without the knowledge of the entire network, any node’s global influence can be accurately measured using this characteristic number, which is independent of the network size. This motivates an efficient algorithm with constant time complexity on the long-standing problem of best seed spreaders selection, with performance remarkably close to the true optimum.

scholarly journals A novel approach for fast and effective influence spread maximization in social networks

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Computational Time ◽  
Maximization Problem ◽  
Influence Spread ◽  
The Social ◽  
Spread Of Influence ◽  
Influence Maximization Problem

The main characteristic of social networks is their ability to quickly spread information between a large group of people. This phenomenon is generated by the social influence that individuals induce on each other.<br>The widespread use of online social networks (e.g., Facebook) increases researchers' interest in how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network).<br>Due to its practical importance in various applications (e.g., viral marketing, target advertisement, personalized recommendation), such a problem has been studied in several variants. Different solution methodologies have been proposed. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. <br>In this context, based on the well-known independent cascade and the linear threshold models of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. Application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, comparison with the best performing state of the art algorithms demonstrates that in large scale scenarios, the proposed approach shows higher performance in terms of influence spread and running time.

The Role of Social Networks in the Viral Marketing of IDM

2012 ◽  
pp. 234-245
Author(s):  
Bhatt Diptee ◽  
Chang Tai Hock ◽  
Wang Lihui ◽  
Ravi S. Sharma
Keyword(s):  
Social Networks ◽  
Behavioral Response ◽  
Word Of Mouth ◽  
Social Ties ◽  
New Products ◽  
Cost Effective ◽  
Vital Role ◽  
Viral Marketing ◽  
Marketing Methods

Social networks are structures consisting of individuals or organizations that enable powerful means of communicating and information sharing. Social networks make viral marketing and word-of mouth (WOM) marketing more effective than before. WOM particularly has received extensive attention in the literature. In this chapter, we discuss the value of social networks in business, especially focusing on the WOM marketing which relies on social ties and preexisting connections to spread marketing messages through a community. We discuss viral marketing using a WOM unit framework. Five qualities of a WOM unit are explained with examples. We illustrate new products and services like the iPhone and relate them with the WOM unit framework. It is recognized that WOM helps businesses spread their marketing message in a cost effective way. We found that WOM marketing plays a vital role in the IDM marketplace and conclude that businesses should actively promote and manage WOM communications using viral marketing methods to achieve desired behavioral response.

scholarly journals A novel approach for fast and effective influence spread maximization in social networks

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Computational Time ◽  
Maximization Problem ◽  
Influence Spread ◽  
The Social ◽  
Spread Of Influence ◽  
Influence Maximization Problem

The main characteristic of social networks is their ability to quickly spread information between a large group of people. This phenomenon is generated by the social influence that individuals induce on each other.<br>The widespread use of online social networks (e.g., Facebook) increases researchers' interest in how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network).<br>Due to its practical importance in various applications (e.g., viral marketing, target advertisement, personalized recommendation), such a problem has been studied in several variants. Different solution methodologies have been proposed. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. <br>In this context, based on the well-known independent cascade and the linear threshold models of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. Application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, comparison with the best performing state of the art algorithms demonstrates that in large scale scenarios, the proposed approach shows higher performance in terms of influence spread and running time.

scholarly journals A novel state space reduction algorithm for team formation in social networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0259786
Author(s):  
Muhammad Zubair Rehman ◽  
Kamal Z. Zamli ◽  
Mubarak Almutairi ◽  
Haruna Chiroma ◽  
Muhammad Aamir ◽  
...  
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Large Data ◽  
Cost Effective ◽  
Communication Cost ◽  
Team Formation ◽  
Graph Reduction ◽  
Effective Teams ◽  
Space Reduction ◽  
Benchmark Datasets

Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF’s. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts–resulting in the formation of more communicative teams with high expertise levels.

scholarly journals Marketing in a socially connected world: the impact of value on sharing of commercial videos

2019 ◽  
Vol 19 (53) ◽  
pp. 5-21
Author(s):  
Cid Gonçalves Filho ◽  
Gustavo Quiroga Souki ◽  
Daniel Fagundes Randt ◽  
Flávia Braga Chinelato
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Online Communication ◽  
Viral Marketing ◽  
Video Sharing ◽  
Video Viewing ◽  
The Social ◽  
Everyday Action ◽  
Entertainment Value ◽  
The Impact

The viral marketing offers answers for the structuring and disseminating fast and large-scale information in favor of content, products, and their brands. Sustained by the growth of technological users, social networks and mobile technology, video viewing, posts, and sharing, it has become an everyday action. Thus, the organizations started to produce commercial videos and dissemination them in the social networks, where consumer users share what they identify themselves with.  Lister (2018) highlights that a video that is socially shared generates 1.200% more shares than the text and images combined. Video is a trend in terms of online communication, as millions of dollars are spent on these efforts to persuade and generate an impact on their audiences target monthly (Lister, 2018). However, most of the studies about video sharing are related to consumer content, not firm generated content. In this sense, the central objective of this study is to identify the antecedents of commercial video sharing and its impact on the consumers' attitudes. The videos that were mostly seen on YouTube in 2017 and the top of mind brands were selected as the research's corpus. A total of 368 questionnaires were collected, preceded by the viewing of the videos that were selected. The results reveal significant impacts of the entertainment value and utility value with the intention of sharing videos, but the social value has no significant impact. In this sense, this study contributed by identifying content and persuasion strategies for firms in order to earn media from sharing of commercial videos, which every day more represent a larger share in the organizations' communication budget.

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