scholarly journals Joint Selection of Influential Users and Locations under Target Region in Location-Based Social Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 709
Author(s):  
Khurshed Ali ◽  
Cheng-Te Li ◽  
Yi-Shin Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Maximization Problem ◽  
Target Region ◽  
Specific Location ◽  
Influential Users ◽  
Location Based Social Networks ◽  
Location Type ◽  
Selection Of ◽  
Product Promotion

Influence Maximization problem, selection of a set of users in a social network to maximize the influence spread, has received ample research attention in the social network analysis domain due to its practical applications. Although the problem has been extensively studied, existing works have neglected the location’s popularity and importance along with influential users for product promotion at a particular region in Location-based Social Networks. Real-world marketing companies are more interested in finding suitable locations and influential users in a city to promote their product and attract as many users as possible. In this work, we study the joint selection of influential users and locations within a target region from two complementary perspectives; general and specific location type selection perspectives. The first is to find influential users and locations at a specified region irrespective of location type or category. The second perspective is to recommend locations matching location preference in addition to the target region for product promotion. To address general and specific location recommendations and influential users, we propose heuristic-based methods that effectively find influential users and locations for product promotion. Our experimental results show that it is not always an optimal choice to recommend locations with the highest popularity values, such as ratings, check-ins, and so, which may not be a true indicator of location popularity to be considered for marketing. Our results show that not only influential users are helpful for product promotion, but suitable influential locations can also assist in promoting products in the target region.

Data Mining Location-Based Social Networks for Geospatial Discovery

2013 ◽  
pp. 2006-2019 ◽  
Author(s):  
Edward Pultar
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Geographical Location ◽  
Geographic Information ◽  
Space Time ◽  
Specific Location ◽  
Local Residents ◽  
Location Based Social Network ◽  
Location Based Social Networks

Modern, Internet-based social networks contain a wealth of information about each member. An integral part of an individual’s online profile is their Volunteered Geographic Information (VGI) such as a user’s current geographical location. Social network members in different cities, countries, or continents engage in different activities due to accessibility, economy, culture, or other factors. The work here focuses on data mining separate groups of social network profiles according to their geography in order to discover information about a place. This results in keywords associated with a specific location and provides an automated way to describe a place in an up to date fashion based upon its current local residents. Location-Based Social Network (LBSN) profiles from four different places are analyzed here and the results are presented as they relate to space, time, and activities.

scholarly journals A quick GRASP-based method for influence maximization in social networks

2021 ◽  
Author(s):  
Isaac Lozano-Osorio ◽  
Jesús Sánchez-Oro ◽  
Abraham Duarte ◽  
Óscar Cordón
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Local Search ◽  
Computing Time ◽  
Exact Algorithm ◽  
Influence Maximization ◽  
Computational Effort ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Influential Users

AbstractThe evolution and spread of social networks have attracted the interest of the scientific community in the last few years. Specifically, several new interesting problems, which are hard to solve, have arisen in the context of viral marketing, disease analysis, and influence analysis, among others. Companies and researchers try to find the elements that maximize profit, stop pandemics, etc. This family of problems is collected under the term Social Network Influence Maximization problem (SNIMP), whose goal is to find the most influential users (commonly known as seeds) in a social network, simulating an influence diffusion model. SNIMP is known to be an $$\mathcal {NP}$$ NP -hard problem and, therefore, an exact algorithm is not suitable for solving it optimally in reasonable computing time. The main drawback of this optimization problem lies on the computational effort required to evaluate a solution. Since each node is infected with a certain probability, the objective function value must be calculated through a Monte Carlo simulation, resulting in a computationally complex process. The current proposal tries to overcome this limitation by considering a metaheuristic algorithm based on the Greedy Randomized Adaptive Search Procedure (GRASP) framework to design a quick solution procedure for the SNIMP. Our method consists of two distinct stages: construction and local search. The former is based on static features of the network, which notably increases its efficiency since it does not require to perform any simulation during construction. The latter involves a local search based on an intelligent neighborhood exploration strategy to find the most influential users based on swap moves, also aiming for an efficient processing. Experiments performed on 7 well-known social network datasets with 5 different seed set sizes confirm that the proposed algorithm is able to provide competitive results in terms of quality and computing time when comparing it with the best algorithms found in the state of the art.

Data Mining Location-Based Social Networks for Geospatial Discovery

2012 ◽  
pp. 204-218
Author(s):  
Edward Pultar
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Geographical Location ◽  
Geographic Information ◽  
Space Time ◽  
Specific Location ◽  
Local Residents ◽  
Location Based Social Network ◽  
Location Based Social Networks

Modern, Internet-based social networks contain a wealth of information about each member. An integral part of an individual’s online profile is their Volunteered Geographic Information (VGI) such as a user’s current geographical location. Social network members in different cities, countries, or continents engage in different activities due to accessibility, economy, culture, or other factors. The work here focuses on data mining separate groups of social network profiles according to their geography in order to discover information about a place. This results in keywords associated with a specific location and provides an automated way to describe a place in an up to date fashion based upon its current local residents. Location-Based Social Network (LBSN) profiles from four different places are analyzed here and the results are presented as they relate to space, time, and activities.

scholarly journals Influence Maximization in Social Networks using Deterministic Crowding Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 4258-4265
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Linear Threshold ◽  
The Social ◽  
Spread Of Influence ◽  
Influential Users ◽  
High Influence ◽  
Influence Maximization Problem

In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others' behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works.

scholarly journals Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect

2019 ◽  
Vol 11 (4) ◽  
pp. 95
Author(s):  
Wang ◽  
Zhu ◽  
Liu ◽  
Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Memory Effect ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Social Reinforcement ◽  
Reinforcement Effect ◽  
The Social ◽  
Influence Maximization Problem

Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A lot of algorithms have been proposed to solve this problem. Recently, in order to achieve more realistic viral marketing scenarios, some constrained versions of influence maximization, which consider time constraints, budget constraints and so on, have been proposed. However, none of them considers the memory effect and the social reinforcement effect, which are ubiquitous properties of social networks. In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects. We first propose a novel propagation model to capture the dynamics of the memory and social reinforcement effects. Then, we modify two baseline algorithms and design a new algorithm to solve the problem under the model. Experiments show that our algorithm achieves the best performance with relatively low time complexity. We also demonstrate that the new version captures some important properties of viral marketing in social networks, such as such as social reinforcements, and could explain some phenomena that cannot be explained by existing influence maximization problem definitions.

scholarly journals You followed my bot! Transforming robots into influential users in Twitter

First Monday ◽  
2013 ◽  
Author(s):  
Johnnatan Messias ◽  
Lucas Schmidt ◽  
Ricardo Oliveira ◽  
Fabrício Benevenuto
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Two Systems ◽  
Scientific Papers ◽  
The Media ◽  
Influential Users

Systems that classify influential users in social networks have been used frequently and are referenced in scientific papers and in the media as an ideal standard of evaluation of influence in the Twitter social network. We consider such systems of evaluation to be complex and subjective, and we therefore suspect that they are vulnerable and easy to manipulate. Based on this, we performed experiments and analysis of two systems for ranking influence: Klout and Twitalyzer. We created simple robots capable of interacting by means of Twitter accounts, and we measured how influent they were. Our results show that it is possible to become influential through simple strategies. This suggests that the systems do not have ideal means to measure and classify influence.

scholarly journals Correlation between Triadic Closure and Homophily Formed over Location-Based Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Nauman Ali Khan ◽  
Wuyang Zhou ◽  
Mudassar Ali Khan ◽  
Ahmad Almogren ◽  
Ikram Ud Din
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Research Direction ◽  
Social Actors ◽  
The Social ◽  
Depth Analysis ◽  
Location Based Social Network ◽  
Triadic Closure ◽  
Location Based Social Networks ◽  
The Given

Social Internet of Things (SIoT) is a variation of social networks that adopt the property of peer-to-peer networks, in which connections between the things and social actors are automatically established. SIoT is a part of various organizations that inherit the social interaction, and these organizations include industries, institutions, and other establishments. Triadic closure and homophily are the most commonly used measures to investigate social networks’ formation and nature, where both measures are used exclusively or with statistical models. The triadic closure patterns are mapped for actors’ communication behavior over a location-based social network, affecting the homophily. In this study, we investigate triads emergence in homophilic social networks. This evaluation is based on the empirical review of triads within social networks (SNs) formed on Big Data. We utilized a large location-based dataset for an in-depth analysis, the Chinese telecommunication-based anonymized call detail records (CDRs). Two other openly available datasets, Brightkite and Gowalla, were also studied. We identified and proposed three social triad classes in a homophilic network to feature the correlation between social triads and homophily. The study opened a promising research direction that relates the variation of homophily based on closure triads nature. The homophilic triads are further categorized into transitive and intransitive groups. As our concluding research objective, we examined the relative triadic throughput within a location-based social network for the given datasets. The research study attains significant results highlighting the positive connection between homophily and a specific social triad class.

scholarly journals Location prediction in location-based social networks

2017 ◽  
Vol 7 (3) ◽  
pp. 149-156
Author(s):  
Mucahit Baydar ◽  
Songul Albayrak
Keyword(s):  
Social Networks ◽  
Wireless Networks ◽  
Social Network ◽  
Mobile Devices ◽  
New Method ◽  
Time Range ◽  
Location Prediction ◽  
The Social ◽  
Location Based Social Network ◽  
Location Based Social Networks

AbstractDevelopments in mobile devices and wireless networks have led to the increasing popularity of location-based social networks. These networks allow users to explore new places, share their location, videos and photos and make friends. They give information about the mobility of users, which can be used to improve the networks. This paper studies the problem of predicting the next check-in of users of location-based social networks. For an accurate prediction, we first analyse the datasets that are obtained from the social networks, Foursquare and Gowalla. Then we obtain some features like place popularity, place popular time range, place distance to user’s home, user’s past visits, category preferences and friendships ,which are used for prediction and deeper understanding of the user behaviours. We use each feature individually, and then in combination, using the new method. Finally, we compare the acquired results and observe the improvement with the new method.Keywords: Location prediction, location-based social network, check-in data.

scholarly journals Metrics for Personal Profiles of Social Network Users

2020 ◽  
Vol 79 ◽  
pp. 01012
Author(s):  
Konstantin Sergeevich Nikolaev ◽  
Fail Mubarakovich Gafarov ◽  
Pavel Nikolaevich Ustin
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Web Application ◽  
Machine Learning Methods ◽  
Processing Data ◽  
Behavioral Predictors ◽  
Technical Details ◽  
Genre Preferences ◽  
Selection Of

This paper discusses the technical details of obtaining and processing data to determine a set of characteristics of texts from social networks, genre preferences in movies and music genres for students of Kazan Federal University who have different academic performance (successful, average, not-successful). The selection of such characteristics is carried out using machine learning methods (Word2Vec, tSNE). The data obtained is used in the development of a functional psychometric model of cognitive behavioral predictors of an individual’s activity within the framework of their educational activities. We also developed a web application for visualizing the obtained data using the Flask engine.

scholarly journals The technology of social networks and people management in the recruitment and selection of its employees

2021 ◽  
pp. 74-90
Author(s):  
Ramiro Rodrigues Sumar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Scientific Evidence ◽  
Face To Face ◽  
The Social ◽  
Recruitment And Selection ◽  
The Impact ◽  
A Company ◽  
Virtual Mode ◽  
Selection Of

Objective: To describe the impact that social networks can have on the recruitment and selection of their employees. Question Problem: How can the social network favor the recruitment and selection of employees of a company? Methodology: Literature review. Results: The evidence of the results showed that technologies through social networks can be relevant for the recruitment and selection of people for the organization. But this recruitment should be done with a differentiated look at each type of social network by the recruiter. Final Considerations: Recruitment and selection have been changing as a traditional (face-to-face) way for the technological (virtual) mode. The study mentioned that social networks are tools capable of bringing to the recruiter candidates able to take the organization responsibly and that there are no barriers in the virtual world to find the ideal candidate. It is emphasized the importance of extending this study based on scientific evidence, in which research can be carried out in companies for the use of social networks in the monitoring of their employees.

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