Certain models of granular computing based on rough fuzzy approximations

2020 ◽  
Vol 39 (3) ◽  
pp. 2797-2816
Author(s):  
Muhammad Akram ◽  
Anam Luqman ◽  
Ahmad N. Al-Kenani
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Problem Solving ◽  
Granular Computing ◽  
Research Study ◽  
Network Models ◽  
Mathematical Framework ◽  
Human Reasoning ◽  
Accuracy Measures ◽  
Lower And Upper Approximations

An extraction of granular structures using graphs is a powerful mathematical framework in human reasoning and problem solving. The visual representation of a graph and the merits of multilevel or multiview of granular structures suggest the more effective and advantageous techniques of problem solving. In this research study, we apply the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures. We discuss the accuracy measures of rough fuzzy approximations and measure the distance between lower and upper approximations. Moreover, we consider the adjacency matrix of a rough fuzzy digraph as an information table and determine certain indiscernible relations. We also discuss some general geometric properties of these indiscernible relations. Further, we discuss the granulation of certain social network models using rough fuzzy digraphs. Finally, we develop and implement some algorithms of our proposed models to granulate these social networks.

Knowledge Discovery from Online Communities

Data Mining ◽  
2013 ◽  
pp. 1230-1252
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
User Behavior ◽  
Network Models ◽  
Social Model ◽  
User Generated Content ◽  
Growth Trend ◽  
User Behaviors ◽  
Application Systems

This chapter presents an overview of social network features such as user behavior, social models, and user-generated content to highlight the most notable research trends and application systems built over such appealing models and online media data. It first describes the most popular social networks by analyzing the growth trend, the user behaviors, the evolution of social groups and models, and the most relevant types of data continuously generated and updated by the users. Next, the most recent and valuable applications of data mining techniques to social network models and user-generated content are presented. Discussed works address both social model extractions tailored to semantic knowledge inference and automatic understanding of the user-generated content. Finally, prospects of data mining research on social networks are provided as well.

Modification of host social networks by manipulative parasites

Behaviour ◽  
2018 ◽  
Vol 155 (7-9) ◽  
pp. 671-688 ◽  
Author(s):  
Robert Poulin
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Mating Behaviour ◽  
Group Formation ◽  
Network Models ◽  
Parasite Transmission ◽  
Activity Levels ◽  
Parasite Infections ◽  
Experimental Approaches ◽  
Host Behaviour

Abstract Social network models provide a powerful tool to estimate infection risk for individual hosts and track parasite transmission through host populations. Here, bringing together concepts from social network theory, animal personality, and parasite manipulation of host behaviour, I argue that not only are social networks shaping parasite transmission, but parasites in turn shape social networks through their effects on the behaviour of infected individuals. Firstly, I review five general categories of behaviour (mating behaviour, aggressiveness, activity levels, spatial distribution, and group formation) that are closely tied to social networks, and provide evidence that parasites can affect all of them. Secondly, I describe scenarios in which behaviour-altering parasites can modify either the role or position of individual hosts within their social network, or various structural properties (e.g., connectance, modularity) of the entire network. Experimental approaches allowing comparisons of social networks pre- versus post-infection are a promising avenue to explore the feedback loop between social networks and parasite infections.

scholarly journals Complex Neutrosophic Hypergraphs: New Social Network Models

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 234 ◽  
Author(s):  
Anam Luqman ◽  
Muhammad Akram ◽  
Florentin Smarandache
Keyword(s):  
Social Networks ◽  
Sustainable Development ◽  
Social Network ◽  
Complex Systems ◽  
Network Models ◽  
Neutrosophic Set ◽  
Neutrosophic Sets ◽  
Score Functions ◽  
Proposed Model ◽  
Complex Valued

A complex neutrosophic set is a useful model to handle indeterminate situations with a periodic nature. This is characterized by truth, indeterminacy, and falsity degrees which are the combination of real-valued amplitude terms and complex-valued phase terms. Hypergraphs are objects that enable us to dig out invisible connections between the underlying structures of complex systems such as those leading to sustainable development. In this paper, we apply the most fruitful concept of complex neutrosophic sets to theory of hypergraphs. We define complex neutrosophic hypergraphs and discuss their certain properties including lower truncation, upper truncation, and transition levels. Furthermore, we define T-related complex neutrosophic hypergraphs and properties of minimal transversals of complex neutrosophic hypergraphs. Finally, we represent the modeling of certain social networks with intersecting communities through the score functions and choice values of complex neutrosophic hypergraphs. We also give a brief comparison of our proposed model with other existing models.

Visualising Social Networks in Collaborative Environments

2010 ◽  
pp. 817-827
Author(s):  
Stephen T. O’Rourke ◽  
Rafael A. Calvo
Keyword(s):  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Social Networking ◽  
Collaborative Work ◽  
Mathematical Framework ◽  
Web Engineering ◽  
Simple Strategy ◽  
Collaboration Environment ◽  
The Social

Social networking and other Web 2.0 applications are becoming ever more popular, with a staggering growth in the number of users and the amount of data they produce. This trend brings new challenges to the Web engineering community, particularly with regard to how we can help users make sense of all this new data. The success of collaborative work and learning environments will increasingly depend on how well they support users in integrating the data that describes the social aspects of the task and its context. This chapter explores the concept of social networking in a collaboration environment, and presents a simple strategy for developers who wish to provide visualisation functionalities as part of their own application. As an explanatory case study, we describe the development of a social network visualisation (SNV) tool, using software components and data publicly available. The SNV tool is designed to support users of a collaborative application by facilitating the exploration of interactions from a network perspective. Since social networks can be large and complex, graph theory is commonly used as a mathematical framework. Our SNV tool integrates techniques from social networking and graph theory, including the filtering and clustering of data, in this case, from a large email dataset. These functions help to facilitate the analysis of the social network and reveal the embedded patterns of user behaviour in the underlying data.

Bridging Provider-Centric and User-Centric Social Networks

2012 ◽  
pp. 63-83 ◽  
Author(s):  
José C. Delgado
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Models ◽  
Migration Path ◽  
The Social ◽  
Active Service ◽  
Web Access ◽  
Close Cooperation ◽  
User Centric

Current social networks are centralized and driven by the providers’ formats, policies, and rules. Subscribing to several networks usually implies duplicating profile information and the effort of replicating changes when needed. Recently, there have been several proposals to support decentralized social networks, but these maintain the client-server paradigm. This chapter recognizes that the user is no longer a mere consumer, but rather a producer, and calls for a paradigm shift, with the user at the center of the social network scenarios, taking the role of an active service, in equal terms with social network providers. This leads to a unified user model: both individual and institutional entities are both users and providers and share the same protocols, although with different emphasis. We call this the user-centric approach and show a migration path from current social network models. To support this approach, we present a new Web access device, the browserver, which includes a browser and a server working in close cooperation, with the goal of replacing the classical browser but being backwards compatible with it to ease the migration path.

A MATHEMATICAL EXTENSION OF ROUGH SET-BASED ISSUES TOWARD UNCERTAIN INFORMATION ANALYSIS

2011 ◽  
Vol 07 (03) ◽  
pp. 543-570 ◽  
Author(s):  
HIROSHI SAKAI ◽  
KOHEI HAYASHI ◽  
MICHINORI NAKATA ◽  
DOMINIK ŚLȨZAK
Keyword(s):  
Rough Set ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Numerical Data ◽  
Mathematical Framework ◽  
Uncertain Information ◽  
Mathematical Properties ◽  
Data Tables ◽  
Table Data ◽  
Lower And Upper Approximations

Rough set theory was originally proposed for analyzing data gathered in data tables, often referred to as information systems. The lower and upper approximations introduced within this theory are known as the very useful concepts. The theory as a whole now becomes a recognized foundation for granular computing. This paper investigates the rough set-based issues for analyzing table data with uncertainty. In reality, tables with non-deterministic information are focused on instead of tables with deterministic information, and several mathematical properties are examined. Especially, decision rule generation from tables with non-deterministic information is highlighted. This investigation is also applied to tables with uncertain numerical data. As a result, a new mathematical framework for analyzing tables with uncertain information is formalized.

scholarly journals Networks: A

2021 ◽  
pp. 71-92
Author(s):  
Jean Walrand
Keyword(s):  
Social Networks ◽  
Communication Network ◽  
Social Network ◽  
Queueing Networks ◽  
Classical Result ◽  
Network Models ◽  
Product Form ◽  
The Internet ◽  
Free Products ◽  
Exchange Information

AbstractSocial networks connect people and enable them to exchange information. News and rumors spread through these networks. We explore models of such propagations. The technology behind social networks is the internet where packets travel from queue to queue. We explain some key results about queueing networks.Section 5.1 explores a model of how rumors spread in a social network. Epidemiologists use similar models to study the spread of viruses. Section 5.2 explains the cascade of choices in a social network where one person’s choice is influenced by those of people she knows. Section 5.3 shows how seeding the market with advertising or free products affects adoptions. Section 5.4 studies a model of how media can influence the eventual consensus in a social network. Section 5.5 explores the randomness of the consensus in a group. Sections 5.6 and 5.7 present a different class of network models where customers queue for service. Section 5.6 studies a single queue and Sect. 5.7 analyzes a network of queues. Section 5.8 explains a classical optimization problem in a communication network: how to choose the capacities of different links. Section 5.9 discusses the suitability of queueing networks as models of the internet. Section 5.10 presents a classical result about a class of queueing networks known as product-form networks.

scholarly journals The influence of negative consumer reviews in social networks

2016 ◽  
Vol 4 (2) ◽  
pp. 44-58 ◽  
Author(s):  
Ruti Gafni ◽  
Osnat Tal Golan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Research Study ◽  
Purchase Decisions ◽  
Sources Of Information ◽  
Consumer Reviews ◽  
The Social ◽  
Facebook Pages ◽  
Buying Decisions

The social networking revolution allows people to share their opinions with their surrounding society, enabling the ability to influence others. Large amounts of consumer reviews are posted on social networks, expressing experiences, either positive or negative, regarding products/services. These reviews are instantly distributed within a huge network of consumers, challenging the firms' managers who need to cope with that. This research study examines the phenomenon of consumers' reviews posted on social networks to measure the influence of negative reviews on the reader's buying decisions and on the firms' attitudes. This research study examines if there are differences between active users, who post and share reviews, and passive users who only read what others posted. This research study was performed merging three sources of information: (1) monitoring consumer posts on three Facebook pages during six months; (2) performing a relevant questionnaire among 201 respondents, and (3) checking the related firms' reaction to those posts. The findings revealed that potential consumers base their decisions on posted reviews; they are exposed to negative reviews that affect their purchase decisions, incoherently to the manner they use the social network (active or passive users), while the firms mostly react, in order to diminish their influence.

EMPIRICAL ANALYSIS OF ATTENTION BEHAVIORS IN ONLINE SOCIAL NETWORKS

2010 ◽  
Vol 21 (07) ◽  
pp. 955-971 ◽  
Author(s):  
FANG DU ◽  
QI XUAN ◽  
TIE-JUN WU
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Metric Space ◽  
Online Social Networks ◽  
Link Prediction ◽  
Network Models ◽  
Friendship Network ◽  
Social Significance ◽  
Near Future ◽  
Type Of Behavior

Studying attention behavior has its social significance because such behavior is considered to lead the evolution of the friendship network. However, this type of behavior in social networks has attracted relatively little attention before, which is mainly because, in reality, such behaviors are always transitory and rarely recorded. In this paper, we collected the attention behaviors as well as the friendship network from Douban database and then carefully studied the attention behaviors in the friendship network as a latent metric space. The revealed similar patterns of attention behavior and friendship suggest that attention behavior may be the pre-stage of friendship to a certain extent, which can be further validated by the fact that pairwise nodes in Douban network connected by attention links beforehand are indeed far more likely to be connected by friendship links in the near future. This phenomenon can also be used to explain the high clustering of many social networks. More interestingly, it seems that attention behaviors are more likely to take place between individuals who have more mutual friends as well as more different friends, which seems a little different from the principles of many link prediction algorithms. Moreover, it is also found that forward attention is preferred to inverse attention, which is quite natural because, usually, an individual must be more interested in others that he is paying attention to than those paying attention to him. All of these findings can be used to guide the design of more appropriate social network models in the future.

scholarly journals Experiencing the Detection of Radicalized Criminals on Facebook Social Network and Data-related Issues

2020 ◽  
Author(s):  
Andrea Tundis ◽  
Leon Böck ◽  
Victoria Stanilescu ◽  
Max Mühlhäuser
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Problem Solving ◽  
Online Social Networks ◽  
Data Availability ◽  
Digital Tools ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Public Dataset

Online social networks (OSNs) represent powerful digital tools to communicate and quickly disseminate information in a non-official way. As they are freely accessible and easy to use, criminals abuse of them for achieving their purposes, for example, by spreading propaganda and radicalising people. Unfortunately, due to their vast usage, it is not always trivial to identify criminals using them unlawfully. Machine learning techniques have shown benefits in problem solving belonging to different application domains, when, due to the huge dimension in terms of data and variables to consider, it is not feasible their manual assessment. However, since the OSNs domain is relatively young, a variety of issues related to data availability makes it difficult to apply and immediately benefit from such techniques, in supporting the detection of criminals on OSNs. In this perspective, this paper wants to share the experience conducted in using a public dataset containing information related to criminals in order to both (i) extract specific features and to build a model for the detection of terrorists on Facebook social network, and (ii) to highlight the current limits. The research methodology as well as the gathered results are fully presented and then the data-related issues, emerged from this experience, are discussed. .

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