scholarly journals Creativity in temporal social networks: how divergent thinking is impacted by one’s choice of peers

2020 ◽  
Vol 17 (171) ◽  
pp. 20200667
Author(s):  
Raiyan Abdul Baten ◽  
Daryl Bagley ◽  
Ashely Tenesaca ◽  
Famous Clark ◽  
James P. Bagrow ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Divergent Thinking ◽  
Self Organization ◽  
Agent Based Simulation ◽  
Web Based ◽  
Agent Based ◽  
High Performing ◽  
Simulation Parameters

Creativity is viewed as one of the most important skills in the context of future-of-work. In this paper, we explore how the dynamic (self-organizing) nature of social networks impacts the fostering of creative ideas. We run six trials ( N = 288) of a web-based experiment involving divergent ideation tasks. We find that network connections gradually adapt to individual creative performances, as the participants predominantly seek to follow high-performing peers for creative inspirations. We unearth both opportunities and bottlenecks afforded by such self-organization. While exposure to high-performing peers is associated with better creative performances of the followers, we see a counter-effect that choosing to follow the same peers introduces semantic similarities in the followers’ ideas. We formulate an agent-based simulation model to capture these intuitions in a tractable manner, and experiment with corner cases of various simulation parameters to assess the generality of the findings. Our findings may help design large-scale interventions to improve the creative aptitude of people interacting in a social network.

scholarly journals Distributed recommender system using multi-agent for social networks

2021 ◽  
Author(s):  
Lubaid Ahmed
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommender Systems ◽  
Recommender System ◽  
Network Simulator ◽  
Clustering Methods ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Data Mining Algorithms ◽  
Multi Agent

Social networks have become significant tools due to the vast and useful information existing in them. The social platforms also act as the storage of entered choices of millions of users for various applications such as political surveys, research studies, marketing product preferences and many more. Social network recommender systems exploit this information and direct users in selecting their choices. It is clear that recommender systems should be efficient enough to be able to process the huge magnitude of data that has been generated in recent years by social network users. This research proposes a foundation of an efficient and scalable recommender system to be able to process large amount of data (i.e. Big data) in a short amount of time. The main goal is providing scalability and efficiency of the recommender system. The simulation of the prototype of such a distributed recommender system by using multi-agent based technologies shows promising results. These prototypes provide recommendations to users about other users with the similar interests in online and distributed manner as real recommender systems. The agents can simulate users or can be used as the containers of algorithms for comparing the similarity between users by different approaches, such as cosine similarity and clustering methods for testing and examining real scenarios. To be able to test these prototypes in agent-based simulation environment an agent-based framework is developed. This framework has three modules named social network crawler, social network simulator and employed prototype of the distributed recommender system that use different text and data mining algorithms. Finally, newly developed performance metric (called Scalability Factor) is introduced that shows the minimum number of servers needed to be able to run the agent systems in parallel. This thesis shows using a distributed and parallel model for recommender systems is the key to increase the speed of recommendation convergence and as a result to provide scalability. Multi-agent based simulation results, coupled with numerical analysis affirm that the proposed solution provides scalability and efficiency for recommender systems.

scholarly journals Distributed recommender system using multi-agent for social networks

2021 ◽  
Author(s):  
Lubaid Ahmed
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommender Systems ◽  
Recommender System ◽  
Network Simulator ◽  
Clustering Methods ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Data Mining Algorithms ◽  
Multi Agent

Social networks have become significant tools due to the vast and useful information existing in them. The social platforms also act as the storage of entered choices of millions of users for various applications such as political surveys, research studies, marketing product preferences and many more. Social network recommender systems exploit this information and direct users in selecting their choices. It is clear that recommender systems should be efficient enough to be able to process the huge magnitude of data that has been generated in recent years by social network users. This research proposes a foundation of an efficient and scalable recommender system to be able to process large amount of data (i.e. Big data) in a short amount of time. The main goal is providing scalability and efficiency of the recommender system. The simulation of the prototype of such a distributed recommender system by using multi-agent based technologies shows promising results. These prototypes provide recommendations to users about other users with the similar interests in online and distributed manner as real recommender systems. The agents can simulate users or can be used as the containers of algorithms for comparing the similarity between users by different approaches, such as cosine similarity and clustering methods for testing and examining real scenarios. To be able to test these prototypes in agent-based simulation environment an agent-based framework is developed. This framework has three modules named social network crawler, social network simulator and employed prototype of the distributed recommender system that use different text and data mining algorithms. Finally, newly developed performance metric (called Scalability Factor) is introduced that shows the minimum number of servers needed to be able to run the agent systems in parallel. This thesis shows using a distributed and parallel model for recommender systems is the key to increase the speed of recommendation convergence and as a result to provide scalability. Multi-agent based simulation results, coupled with numerical analysis affirm that the proposed solution provides scalability and efficiency for recommender systems.

scholarly journals Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Jun Long ◽  
Lei Zhu ◽  
Zhan Yang ◽  
Chengyuan Zhang ◽  
Xinpan Yuan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Multimedia Data ◽  
Attribute Information ◽  
Novel Method ◽  
Relationship Network ◽  
Heterogeneous Social Networks ◽  
The Relationship ◽  
Over Time

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

Analyzing Social Networks to Mine Important Friends

2013 ◽  
pp. 90-104 ◽  
Author(s):  
Carson K.-S. Leung ◽  
Irish J. M. Medina ◽  
Syed K. Tanbeer
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Social Networking Sites ◽  
Web Based ◽  
The Social ◽  
Social Entity ◽  
Social Entities ◽  
Media Data ◽  
Media Mining

The emergence of Web-based communities and social networking sites has led to a vast volume of social media data, embedded in which are rich sets of meaningful knowledge about the social networks. Social media mining and social network analysis help to find a systematic method or process for examining social networks and for identifying, extracting, representing, and exploiting meaningful knowledge—such as interdependency relationships among social entities in the networks—from the social media. This chapter presents a system for analyzing the social networks to mine important groups of friends in the networks. Such a system uses a tree-based mining approach to discover important friend groups of each social entity and to discover friend groups that are important to social entities in the entire social network.

Building Recommendation Service with Social Networks and Semantic Databases

2014 ◽  
pp. 83-92
Author(s):  
Sašo Karakatič ◽  
Vili Podgorelec ◽  
Marjan Heričko
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Semantic Information ◽  
Additional Data ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Web Based ◽  
Semantic Database

In this chapter, it is shown how useful user services can be created through the integration of social networks and semantic databases. The authors developed a recommendation service in a form of a Web-based application, where a user's interests are imported from social network Facebook and linked with additional data from open semantic database Freebase. Based on a custom implementation of k-nearest neighbors algorithm, the developed method is able to find recommendations based on users’ interests enriched with semantic information. The resulting list of found recommendations is then shown to the user in some basic categories like movies, music, games, books, and others.

Visualizing Co-Authorship Social Networks and Collaboration Recommendations With CNARe

2018 ◽  
pp. 173-188
Author(s):  
Michele A. Brandão ◽  
Matheus A. Diniz ◽  
Guilherme A. de Sousa ◽  
Mirella M. Moro
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Online Tool ◽  
Strength Of Ties ◽  
E Learning

Studies have analyzed social networks considering a plethora of metrics for different goals, from improving e-learning to recommend people and things. Here, we focus on large-scale social networks defined by researchers and their common published articles, which form co-authorship social networks. Then, we introduce CNARe, an online tool that analyzes the networks and present recommendations of collaborations based on three different algorithms (Affin, CORALS and MVCWalker). Through visualizations and social networks metrics, CNARe also allows to investigate how the recommendations affect the co-authorship social networks, how researchers' networks are in a central and eagle-eye context, and how the strength of ties behaves in large co-authorship social networks. Furthermore, users can upload their own network in CNARe and make their own recommendation and social network analysis.

A Usability Evaluation of Facebook's Privacy Features Based on the Perspectives of Experts and Users

2019 ◽  
pp. 1544-1568
Author(s):  
Márcio J. Mantau ◽  
Marcos H. Kimura ◽  
Isabela Gasparini ◽  
Carla D. M. Berkenbrock ◽  
Avanilde Kemczinski
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
User Interaction ◽  
Usability Evaluation ◽  
Second Step ◽  
Web Based ◽  
The Third ◽  
Privacy Settings ◽  
Three Stages

The issue of privacy in social networks is a hot topic today, because of the growing amount of information shared among users, who are connected to social media every moment and by different devices and displays. This chapter presents a usability evaluation of the privacy features of Facebook's social network. The authors carry out an evaluation composed by three approaches, executed in three stages: first by the analysis and inspection of system's features related to privacy, available for both systems (Web-based systems and mobile-based systems, e.g. app). The second step is a heuristic evaluation led by three experts, and finally, the third step is a questionnaire with 605 users to compare the results between specialists and real users. This chapter aims to present the problems associated with these privacy settings, and it also wants to contribute for improving the user interaction with this social network.

Extending Social Network Diagrams for Large Scale Collaboration

2012 ◽  
pp. 1-13
Author(s):  
I.T. Hawryszkiewycz
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Web 2.0 ◽  
Large Scale ◽  
Policy Planning ◽  
Web 2.0 Technologies ◽  
Enterprise Social Networks ◽  
Cloud Technologies ◽  
Common Vision ◽  
Network Diagrams

The chapter provides a way for modeling large scale collaboration using an extension to social network diagrams called enterprise social networks (ESNs). The chapter uses the ESN diagrams to describe activities in policy planning and uses these to define the services to be provided by cloud technologies to support large scale collaboration. This chapter describes collaboration by an architecture made up of communities each with a role to ensure that collaboration is sustainable. The architecture is based on the idea of an ensemble of communities all working to a common vision supported by services provided by the collaboration cloud using Web 2.0 technologies.

Large-scale dynamic social data representation for structure feature learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5253-5262
Author(s):  
Xiaoxian Zhang ◽  
Jianpei Zhang ◽  
Jing Yang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Dynamic Change ◽  
Feature Learning ◽  
Data Representation ◽  
Feature Representation ◽  
Small Scale ◽  
Dynamic Feature ◽  
Dynamic Social Networks

The problems caused by network dimension disasters and computational complexity have become an important issue to be solved in the field of social network research. The existing methods for network feature learning are mostly based on static and small-scale assumptions, and there is no modified learning for the unique attributes of social networks. Therefore, existing learning methods cannot adapt to the dynamic and large-scale of current social networks. Even super large scale and other features. This paper mainly studies the feature representation learning of large-scale dynamic social network structure. In this paper, the positive and negative damping sampling of network nodes in different classes is carried out, and the dynamic feature learning method for newly added nodes is constructed, which makes the model feasible for the extraction of structural features of large-scale social networks in the process of dynamic change. The obtained node feature representation has better dynamic robustness. By selecting the real datasets of three large-scale dynamic social networks and the experiments of dynamic link prediction in social networks, it is found that DNPS has achieved a large performance improvement over the benchmark model in terms of prediction accuracy and time efficiency. When the α value is around 0.7, the model effect is optimal.

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