scholarly journals How to make methodological decisions when inferring social networks

2019 ◽  
Author(s):  
André C. Ferreira ◽  
Rita Covas ◽  
Liliana R. Silva ◽  
Sandra C. Esteves ◽  
Inês F. Duarte ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Data Collection ◽  
Network Inference ◽  
A Priori ◽  
Pilot Studies ◽  
Multiple Hypotheses Testing ◽  
Sociable Weavers ◽  
Breeding Groups

ABSTRACTConstructing and analysing social networks data can be challenging. When designing new studies, researchers are confronted with having to make decisions about how data are collected and networks are constructed, and the answers are not always straightforward. The current lack of guidance on building a social network for a new study system might lead researchers to try several different methods, and risk generating false results arising from multiple hypotheses testing. We suggest an approach for making decisions when developing a network without jeopardising the validity of future hypothesis tests. We argue that choosing the best edge definition for a network can be made using a priori knowledge of the species, and testing hypotheses that are known and independent from those that the network will ultimately be used to evaluate. We illustrate this approach by conducting a pilot study with the aim of identifying how to construct a social network for colonies of cooperatively breeding sociable weavers. We first identified two ways of collecting data using different numbers of feeders and three ways to define associations among birds. We then identified which combination of data collection and association definition maximised (i) the assortment of individuals into ‘breeding groups’ (birds that contribute towards the same nest and maintain cohesion when foraging), and (ii) socially differentiated relationships (more strong and weak relationships than expected by chance). Our approach highlights how existing knowledge about a system can be used to help navigate the myriad of methodological decisions about data collection and network inference.SIGNIFICANCE STATEMENTGeneral guidance on how to analyse social networks has been provided in recent papers. However less attention has been given to system-specific methodological decisions when designing new studies, specifically on how data are collected, and how edge weights are defined from the collected data. This lack of guidance can lead researchers into being less critical about their study design and making arbitrary decisions or trying several different methods driven by a given preferred hypothesis of interest without realising the consequences of such approaches. Here we show that pilot studies combined with a priori knowledge of the study species’ social behaviour can greatly facilitate making methodological decisions. Furthermore, we empirically show that different decisions, even if data are collected under the same context (e.g. foraging), can affect the quality of a network.

scholarly journals A priori checking of the light-response and data quality before extended data collection in pump–probe photocrystallography experiments

2017 ◽  
Vol 73 (1) ◽  
pp. 23-26 ◽  
Author(s):  
Philip Coppens ◽  
Anna Makal ◽  
Bertrand Fournier ◽  
Katarzyna N. Jarzembska ◽  
Radosław Kamiński ◽  
...  
Keyword(s):  
Data Collection ◽  
Light Exposure ◽  
A Priori ◽  
Light Response ◽  
Response Ratio ◽  
Full Data ◽  
Pump Probe ◽  
Ratio Correlation ◽  
Induced Signal

In picosecond and slower pump–probe diffraction experiments, collection of response–ratio correlation sets prior to full data collection provides an invaluable confirmation of the existence of a light-induced signal prior to full data collection. If a response to light exposure is observed, the quality of the data being collected can be assessed. A number of such correlation plots both for synchrotron and in-house pump–probe data collection are presented.

scholarly journals Determining the proximity of groups in social networks based on text analysis using big data

2019 ◽  
pp. 521-526
Author(s):  
A S Mukhin ◽  
I A Rytsarev ◽  
R A Paringer ◽  
A V Kupriyanov ◽  
D V Kirsh
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Data Collection ◽  
Text Analysis ◽  
Software Tool ◽  
Text Data ◽  
The Social ◽  
Information Research ◽  
Definition Of

The article is devoted to the definition of such groups in social networks. The object of the study was selected data social network Vk. Text data was collected, processed and analyzed. To solve the problem of obtaining the necessary information, research was conducted in the field of optimization of data collection of the social network Vk. A software tool that provides the collection and subsequent processing of the necessary data from the specified resources has been developed. The existing algorithms of text analysis, mainly of large volume, were investigated and applied.

The Usage of Social Network to Students: Does It Improve Student’s Education Quality During COVID-19 Outbreak

2020 ◽  
Vol 17 (12) ◽  
pp. 5224-5228
Author(s):  
Indraah Kolandaisamy ◽  
Raenu Kolandaisamy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Education Quality ◽  
Quality Of Education ◽  
Online Classes ◽  
Outbreak Period ◽  
Technology Advancement ◽  
The Relationship

In the era of technology advancement and COVID-19 outbreak period, all physical classes have been converted to online classes through social network platforms. Having online classes through social networks are actually very comfortable and flexible for students as they can have their classes at various places. This paper is focuses on the relationship between usages of social network and the quality of education during COVID-19 outbreak.

Social Networks and Terrorism

2011 ◽  
pp. 232-246 ◽  
Author(s):  
David Knoke
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Data Collection ◽  
Case Studies ◽  
Recent Case ◽  
Future Research ◽  
Data Collection And Analysis ◽  
Tools And Methods

This chapter explains how international terror networks, consisting of individuals and organizations spanning countries and continents, form and evolve. It describes tools and methods used by social network analysts to study such networks; their applications by counterterrorist organizations; their limitations and problems in data collection and analysis; and directions for future research. It also discusses a few recent case studies by prominent researchers.

Unraveling the Taste Fabric of Social Networks

2009 ◽  
pp. 1521-1546
Author(s):  
Hugo Liu ◽  
Pattie Maes ◽  
Glorianna Davenport
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Social Connectedness ◽  
Natural Language Text ◽  
Cross Domain ◽  
Semantic Mining ◽  
The Rich ◽  
Language Text

Popular online social networks such as Friendster and MySpace do more than simply reveal the superficial structure of social connectedness—the rich meanings bottled within social network profiles themselves imply deeper patterns of culture and taste. If these latent semantic fabrics of taste could be harvested formally, the resultant resource would afford completely novel ways for representing and reasoning about web users and people in general. This paper narrates the theory and technique of such a feat—the natural language text of 100,000 social network profiles were captured, mapped into a diverse ontology of music, books, films, foods, etc., and machine learning was applied to infer a semantic fabric of taste. Taste fabrics bring us closer to improvisational manipulations of meaning, and afford us at least three semantic functions—the creation of semantically flexible user representations, cross-domain taste-based recommendation, and the computation of taste-similarity between people— whose use cases are demonstrated within the context of three applications—the InterestMap, Ambient Semantics, and IdentityMirror. Finally, we evaluate the quality of the taste fabrics, and distill from this research reusable methodologies and techniques of consequence to the semantic mining and Semantic Web communities.

scholarly journals Self-Management Characterization for Families of Children With Medical Complexity and Their Social Networks: Protocol for a Qualitative Assessment

10.2196/14810 ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. e14810
Author(s):  
Rupa S Valdez ◽  
Christopher Lunsford ◽  
Jiwoon Bae ◽  
Lisa C Letzkus ◽  
Jessica Keim-Malpass
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Data Collection ◽  
Care Coordination ◽  
Support Network ◽  
System Level ◽  
Qualitative Assessment ◽  
Self Management ◽  
Children With Medical Complexity ◽  
Medical Complexity

Background Children with medical complexity (CMC) present rewarding but complex challenges for the health care system. Transforming high-quality care practices for this population requires multiple stakeholders and development of innovative models of care. Importantly, care coordination requires significant self-management by families in home- and community-based settings. Self-management often requires that families of CMC rely on vast and diverse social networks, encompassing both online and offline social relationships with individuals and groups. The result is a support network surrounding the family to help accomplish self-management of medical tasks and care coordination. Objective The goal of this study is to use a theoretically driven perspective to systematically elucidate the range of self-management experiences across families of CMC embedded in diverse social networks and contextual environments. This approach will allow for characterization of the structure and process of self-management of CMC with respect to social networks, both in person and digitally. This research proposal aims to address the significant gaps in the self-management literature surrounding CMC, including the following: (1) how self-management responsibilities are distributed and negotiated among the social network and (2) how individual-, family-, and system-level factors influence self-management approaches for CMC from a theoretically driven perspective. Methods This study will encompass a qualitative descriptive approach to understand self-management practices among CMC and their social networks. Data collection and analysis will be guided by a theoretical and methodological framework, which synthesizes perspectives from nursing, human factors engineering, public health, and family counseling. Data collection will consist of semistructured interviews with children, parents, and social network members, inclusive of individuals such as friends, neighbors, and community members, as well as online communities and individuals. Data analysis will consist of a combination of inductive and deductive methods of qualitative content analysis, which will be analyzed at both individual and multiadic levels, where interview data from two or more individuals, focused on the same experience, will be comparatively analyzed. Results This study will take approximately 18 months to complete. Our long-term goals are to translate the qualitative analysis into (1) health IT design guidance for innovative approaches to self-management and (2) direct policy guidance for families of CMC enrolled in Medicaid and private insurance. Conclusions Multiple innovative components of this study will enable us to gain a comprehensive and nuanced understanding of the lived experience of self-management of CMC. In particular, by synthesizing and applying theoretical and methodological approaches from multiple disciplines, we plan to create novel informatics and policy solutions to support their care within home and community settings. International Registered Report Identifier (IRRID) PRR1-10.2196/14810

scholarly journals Social Network and Healthy Ageing among Elderly in Helping Hearts Old Age Home, Coimbatore

2019 ◽  
Vol 8 (12S) ◽  
pp. 571-576
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Participation ◽  
Meaning In Life ◽  
Well Being ◽  
The Elderly ◽  
Healthy Ageing ◽  
The Social ◽  
The Relationship

In the current times, the research cites that elderly definitely need social networks to aid in their mental and physical well being. The previous researches have indicated familyfocused, friend-focused, and restricted types as the types of social networks available. Social network include social interaction and social communication. It is the need of the hour to study about the social network of the elderly population because many of them are left with nobody and loneliness is one of the important factor not to mention about desertion by their loved ones since they are no longer productive individuals. The heterogeneity of social networks is pathway to successful and healthy ageing. Healthy ageing is about using opportunities so that they can have social participation and lead a good quality of life. Elderly need not be burdensome individuals in the society instead they can be involved in lot of activities which contribute to them ageing gracefully. The research studies state that rural elderly have more chances of social participation that they find more meaning in life which is a contributing factor for healthy ageing. The present study aims to find out the relationship between social network and healthy ageing.

Extracting Influential Nodes in Social Networks on Local Weight Aspect

2016 ◽  
Vol 8 (2) ◽  
pp. 21-35
Author(s):  
Jun Jun Cheng ◽  
Yan Chao Zhang ◽  
Xin Zhou ◽  
Hui Cheng
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Time Complexity ◽  
Heuristic Algorithms ◽  
Transmission Model ◽  
User Interest ◽  
Local Weight ◽  
Weight Index ◽  
Influential Nodes

Studies have shown that influential nodes play an important role in all kinds of dynamic behavior in the complex network. Excavation or recognition of such nodes contributes to the development of application areas such as social network advertising and user interest recommendation. Although some heuristic algorithms such as degree, betweenness, closeness and k-shell (or k-core) can identify influential nodes at the same time, they are disadvantaged in terms of accuracy and time complexity. Based on this, the authors propose a novel local weight index to distinguish the node influence based on the theory of ties strength. This index emphasizes that the node influence is jointly decided by the quantity and quality of the neighbors, and its time complexity is much lower than closeness and betweenness. With the aid of SIR information transmission model, this paper verifies the validity of local weight index.

Unraveling the Taste Fabric of Social Networks

2009 ◽  
pp. 18-43
Author(s):  
Hugo Liu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Social Connectedness ◽  
Natural Language Text ◽  
Cross Domain ◽  
Semantic Mining ◽  
The Rich ◽  
Language Text

Popular online social networks such as Friendster and MySpace do more than simply reveal the superficial structure of social connectedness — the rich meanings bottled within social network profiles themselves imply deeper patterns of culture and taste. If these latent semantic fabrics of taste could be harvested formally, the resultant resource would afford completely novel ways for representing and reasoning about web users and people in general. This paper narrates the theory and technique of such a feat — the natural language text of 100,000 social network profiles were captured, mapped into a diverse ontology of music, books, films, foods, etc., and machine learning was applied to infer a semantic fabric of taste. Taste fabrics bring us closer to improvisational manipulations of meaning, and afford us at least three semantic functions — the creation of semantically flexible user representations, cross-domain taste-based recommendation, and the computation of taste-similarity between people — whose use cases are demonstrated within the context of three applications — the InterestMap, Ambient Semantics, and IdentityMirror. Finally, we evaluate the quality of the taste fabrics, and distill from this research reusable methodologies and techniques of consequence to the semantic mining and Semantic Web communities.

Preventing Identity Disclosure in Social Networks Using Intersected Node

2016 ◽  
Vol 10 (3) ◽  
pp. 25-41 ◽  
Author(s):  
Amardeep Singh ◽  
Divya Bansal ◽  
Sanjeev Sofat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Privacy And Security ◽  
Identity Disclosure ◽  
The Social ◽  
Confidential Data ◽  
Noise Nodes ◽  
Graphical Structure

Social networks like Facebook, Twitter, Pinterest etc. provide data of its users to the demanding organizations to better comprehend the quality of their potential clients. Publishing confidential data of social network users in its raw form raises several privacy and security concerns. Recently, some anonymization techniques have been developed to address these issues. In this paper, a technique to prevent identity disclosure through structure attacks has been proposed which not only prevents identity disclosure but also preserves utility of data published by online social networks. Algorithms have been developed by using noise nodes/edges with the consideration of introducing minimum change in the original graphical structure of social networks. The authors' work is unique in the sense that previous works are based on edge editing only but their proposed work protects against structure attacks using mutual nodes in the social network and the effectiveness of the proposed technique has been proved using APL (Average Path Length) and information loss as parameters.

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