scholarly journals An Exploration of Community Detection and Recommendation Systems (CDR) in Social Networks

2021 ◽  
Vol 10 (10) ◽  
pp. 23-33
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Recommender Systems ◽  
Community Detection ◽  
Graphical Models ◽  
Social Relations ◽  
Local Area ◽  
Network Information ◽  
Detection Techniques

Community detection and Recommender systems are assumed as significant parts in helping the web users discover important information by proposing information of likely interest to them and a central task for network analysis means to segment a network into numerous substructures to assist with uncovering their inactive capacities. Community detection has been widely concentrated in and extensively applied to numerous real world network problems. Because of the possible worth of social relations in recommender systems, social recommendation has drawn in expanding consideration in recent years. As the issues that network strategies attempt to solve and the network information to be determined become progressively more complex, new methodologies have been proposed and created, traditional ways to deal with community detection and recommendation commonly use probabilistic graphical models and implement an assortment of earlier information to deduce community structures. Regardless of all the new progression, there is as yet an absence of astute comprehension of the hypothetical and methodological supporting of local area location, which will be fundamentally significant for future advancement of the space of social network analysis. In this paper, we start by giving conventional meanings of social networks terms and talk about the novel property of social networks and its implications. Unified architecture of network community finding methods to characterize the state-of-the-art of the field of community detection. In particular, we give a complete survey of the current community detection techniques and audit of existing recommender systems examine some exploration bearings to further develop social network capabilities.

Give Attention to Overlapping Network Detection in Networks for Multimedia

2019 ◽  
Vol 16 (8) ◽  
pp. 3173-3177
Author(s):  
Mercy Paul Selvan ◽  
Akansha Gupta ◽  
Anisha Mukherjee
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Recommender Systems ◽  
Small Groups ◽  
Network Nodes ◽  
Network Discovery ◽  
Multimedia Social Networks ◽  
Overlapping Community

Finding overlapping agencies from multimedia social networks is an thrilling and important trouble in records mining and recommender systems but, existing overlapping network discovery often generates overlapping community structures with superfluous small groups. Network detection in a multimedia and social network is a conducive difficulty in the network gadget and it helps to understand and learn the overall network shape in element. Those are essentially the dividing wall of network nodes into a few subgroups in which nodes within these subgroups are densely linked, but the connections are sparser in between the subgroups. Social network analysis is widely widespread domain which draws the attention of many information mining experts. Some wide variety of actual community common characteristics which it shares are facebook, Twitter show off the idea of network shape inside the community. Social network is represented as a community graph. Detecting the groups entails locating the densely linked nodes.

scholarly journals Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL

Information ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 361 ◽  
Author(s):  
Raji Ghawi ◽  
Jürgen Pfeffer
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Social Relations ◽  
World Wide ◽  
Open Data ◽  
Linked Open Data ◽  
The World ◽  
Description Framework ◽  
Resource Description

Linked Open Data (LOD) refers to freely available data on the World Wide Web that are typically represented using the Resource Description Framework (RDF) and standards built on it. LOD is an invaluable resource of information due to its richness and openness, which create new opportunities for many areas of application. In this paper, we address the exploitation of LOD by utilizing SPARQL queries in order to extract social networks among entities. This enables the application of de-facto techniques from Social Network Analysis (SNA) to study social relations and interactions among entities, providing deep insights into their latent social structure.

scholarly journals Sub-Community Graph Retrieval from a Compressed Community Graph using Graph Mining

2019 ◽  
Vol 9 (1) ◽  
pp. 6176-6182
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Graph Mining ◽  
Experimental Results ◽  
Large Community

Community detection and its retrieval is one of the most relevant and important topics in graph mining. Hence it is treated as one of the important applications in the field of social network analysis. Community detection plays an important role in a large community graph by enabling and selecting the desired community’s sub-graph. The proposed algorithm detects and extracts the desired sub-community graph from a compressed community graph for further analysis purpose. The authors present both theoretical and experimental results with three benchmark social networks. The proposed technique is efficient in terms of complexities.

Community Detection in Social Networks

2014 ◽  
Vol 496-500 ◽  
pp. 2174-2177 ◽  
Author(s):  
Chang Su ◽  
Yu Kun Wang ◽  
Yue Yu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Real Network ◽  
The Past

Community detection as a branch of social network analysis has been a hot topic in the past decade. This paper reviews the research about the community detection these years and focuses on the community detection relevant classical algorithms as well as the classic real network datasets.

Set Pair Social Network Analysis Model

2011 ◽  
Vol 50-51 ◽  
pp. 63-67 ◽  
Author(s):  
Hong Mei Yang ◽  
Chun Ying Zhang ◽  
Rui Tao Liang ◽  
Fang Tian
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Dynamic Problems ◽  
Analysis Model ◽  
Network Information ◽  
The Social ◽  
The Relationship

Through the study on social network information, this paper explore that there exists the certain and uncertain phenomena in the process of finding the relationship between individuals by using social networks, and the social networks are constantly changing. In light of there are some uncertainty and dynamic problems for the network, this paper put forward the set pair social network analysis model and set pair social network analysis model and its properties.

A SOCIAL NETWORK ANALYSIS OF THE FOUNDERS OF INSTITUTIONALIZED PUBLIC ACCOUNTANCY

2000 ◽  
Vol 27 (2) ◽  
pp. 1-48 ◽  
Author(s):  
Thomas A. Lee
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relations ◽  
Insurance Industry ◽  
Archival Data ◽  
Research Results ◽  
The Social ◽  
Sources Of Influence

This paper examines the social relations of the founders of the first institutions of modern public accountancy in Scotland. The study uses archival data to construct social networks prior to 1854. Individual founders in the networks are identified as potentially significant sources of influence in the foundation events. The paper reports the social network analysis in several parts. First, relations between the founders of The Institute of Accountants in Edinburgh (IAE), renamed The Society of Accountants in Edinburgh (SAE), are networked. Second, a similar analysis is made of the foundation of The Institute of Accountants and Actuaries in Glasgow (IAAG). Third, social links between individual founders of the IAE/SAE and IAAG are identified. The research results are generally consistent with prior studies but reveal significant matters not identified by other researchers. The social network analysis of the IAE/SAE founders confirms the existence of a cohesive and elite community and the presence of an elite within an elite. There is evidence of strong links to lawyers and landowners, as well as significant links to the insurance industry.

Humans, Plants, and Networks

2016 ◽  
Vol 7 (1) ◽  
pp. 107-128 ◽  
Author(s):  
Laura Calvet-Mir ◽  
Matthieu Salpeteur
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Collection ◽  
Social Relationships ◽  
Social Relations ◽  
Local Ecological Knowledge ◽  
Ecological Knowledge ◽  
Longitudinal Data Collection

ABSTRACTIn recent years, Social Network Analysis (SNA) has increasingly been applied to the study of complex human-plant relations. This quantitative approach has enabled a better understanding of (1) how social networks help explain agrobiodiversity management, and (2) how social relations influence the transmission of local ecological knowledge (LEK) related to plants. In this paper, we critically review the most recent works pertaining to these two lines of research. First, our results show that this fast-developing literature proposes new insights on local agrobiodiversity management mechanisms, as well as on the ways seed exchange systems are articulated around other social relationships, such as kinship. Second, current works show that inter-individual connections affect LEK transmission, the position of individuals in networks being related to the LEK they hold. We conclude by stressing the importance of combining this method with comprehensive approaches and longitudinal data collection to develop deeper insights into human-plant relations.

Introduction

2020 ◽  
pp. xxii-13
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Bibliometric Analysis ◽  
Social Life ◽  
New Directions ◽  
Central Paradigm

This chapter provides an introduction to this volume on social networks. It argues that social network analysis is greater than a method or data, but serves as a central paradigm for understanding social life. The chapter offers evidence of the influence of social network analysis with a bibliometric analysis of research on social networks. This analysis underscores how pervasive network analysis has become and highlights key theoretical and methodological concerns. It also introduces the sections of the volume broadly structured around theory, methods, broad conceptualizations like culture and temporality, and disciplinary contributions. The chapter concludes by discussing several promising new directions in the field of social network analysis.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

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