scholarly journals Tight knit under stress: colony resilience to the loss of tandem leaders during relocation in an Indian ant

2015 ◽  
Vol 2 (9) ◽  
pp. 150104 ◽  
Author(s):  
Swetashree Kolay ◽  
Sumana Annagiri
Keyword(s):  
Network Analysis ◽  
Social Insects ◽  
Network Structure ◽  
Similar Rate ◽  
Tandem Running ◽  
Frequent Event ◽  
Random Removal ◽  
Task Organization

The movement of colonies from one nest to another is a frequent event in the lives of many social insects and is important for their survival and propagation. This goal-oriented task is accomplished by means of tandem running in some ant species, such as Diacamma indicum . Tandem leaders are central to this process as they know the location of the new nest and lead colony members to it. Relocations involving targeted removal of leaders were compared with unmanipulated and random member removal relocations. Behavioural observations were integrated with network analysis to examine the differences in the pattern of task organization at the level of individuals and that of the colony. All colonies completed relocation successfully and leaders who substituted the removed tandem leaders conducted the task at a similar rate having redistributed the task in a less skewed manner. In terms of network structure, this resilience was due to significantly higher density and outcloseness indicating increased interaction between substitute leaders. By contrast, leader–follower interactions and random removal networks showed no discernible changes. Similar explorations of other goal-oriented tasks in other societies will possibly unveil new facets in the interplay between individuals that enable the group to respond effectively to stress.

scholarly journals Bayesian Network Structure and Predictability of Autistic Traits

2020 ◽  
pp. 003329412097815
Author(s):  
Giovanni Briganti ◽  
Donald R. Williams ◽  
Joris Mulder ◽  
Paul Linkowski
Keyword(s):  
Sex Differences ◽  
Network Analysis ◽  
Bayesian Network ◽  
Network Structure ◽  
Network Connectivity ◽  
Autistic Traits ◽  
Data Set ◽  
Conditional Dependence ◽  
Bayesian Network Structure ◽  
Male Subjects

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.

scholarly journals Identifying Integration and Differentiation in a Hospital’s Logistical System: A social network analysis of a case study

2020 ◽  
Author(s):  
Annelies van der Ham ◽  
Frits Van Merode ◽  
Dirk Ruwaard ◽  
Arno Van Raak
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Structure ◽  
Organization Theory ◽  
Exploratory Study ◽  
Case Study Research ◽  
Study Research ◽  
And Performance

Abstract Background Integration, the coordination and alignment of tasks, has been promoted widely in order to improve the performance of hospitals. Both organization theory and social network analysis offer perspectives on integration. This exploratory study research aims to understand how a hospital’s logistical system works, and in particular to what extent there is integration and differentiation. More specifically, it first describes how a hospital organizes logistical processes; second, it identifies the agents and the interactions for organizing logistical processes, and, third, it establishes the extent to which tasks are segmented into subsystems, which is referred to as differentiation, and whether these tasks are coordinated and aligned, thus achieving integration.Methods The study is based on case study research carried out in a hospital in the Netherlands. All logistical tasks that are executed for surgery patients were studied. Using a mixed method, data were collected from the Hospital Information System (HIS), documentation, observations and interviews. These data were used to perform a social network analysis and calculate the network metrics of the hospital network.Results This paper shows that 23 tasks are executed by 635 different agents who interact through 31,499 interaction links. The social network of the hospital demonstrates both integration and differentiation. The network appears to function differently from what is assumed in literature, as the network does not reflect the formal organizational structure of the hospital, and tasks are mainly executed across functional silos. Nurses and physicians perform integrative tasks and two agents who mainly coordinate the tasks in the network, have no hierarchical position towards other agents. The HIS does not seem to fulfill the interactional needs of agents. Conclusions This exploratory study reveals the network structure of a hospital. The cross-functional collaboration, the integration found, and position of managers, coordinators, nurses and doctors suggests a possible gap between organizational perspectives on hospitals and reality. This research sets a basis for further research that should focus on the relation between network structure and performance, on how integration is achieved and in what way organization theory concepts and social network analysis could be used in conjunction with one another.

scholarly journals Network Analysis for Economics and Finance: An Application to Firm Ownership

2021 ◽  
pp. 331-355
Author(s):  
Janina Engel ◽  
Michela Nardo ◽  
Michela Rancan
Keyword(s):  
Network Analysis ◽  
Network Structure ◽  
Giant Component ◽  
Model Data ◽  
Firm Ownership ◽  
Network Metrics ◽  
Different Levels ◽  
Significant Concentration

AbstractIn this chapter, we introduce network analysis as an approach to model data in economics and finance. First, we review the most recent empirical applications using network analysis in economics and finance. Second, we introduce the main network metrics that are useful to describe the overall network structure and characterize the position of a specific node in the network. Third, we model information on firm ownership as a network: firms are the nodes while ownership relationships are the linkages. Data are retrieved from Orbis including information of millions of firms and their shareholders at worldwide level. We describe the necessary steps to construct the highly complex international ownership network. We then analyze its structure and compute the main metrics. We find that it forms a giant component with a significant number of nodes connected to each other. Network statistics show that a limited number of shareholders control many firms, revealing a significant concentration of power. Finally, we show how these measures computed at different levels of granularity (i.e., sector of activity) can provide useful policy insights.

Extending Proximity Measures to Attributed Networks for Community Detection

2021 ◽  
Vol 30 (4) ◽  
pp. 441-455
Author(s):  
Rinat Aynulin ◽  
◽  
Pavel Chebotarev ◽  
◽  
Keyword(s):  
Network Analysis ◽  
Community Detection ◽  
Network Structure ◽  
Real World ◽  
Adjacency Matrix ◽  
Attribute Information ◽  
Node Attributes ◽  
Proximity Measures ◽  
Definition Of ◽  
Attributed Networks

Proximity measures on graphs are extensively used for solving various problems in network analysis, including community detection. Previous studies have considered proximity measures mainly for networks without attributes. However, attribute information, node attributes in particular, allows a more in-depth exploration of the network structure. This paper extends the definition of a number of proximity measures to the case of attributed networks. To take node attributes into account, attribute similarity is embedded into the adjacency matrix. Obtained attribute-aware proximity measures are numerically studied in the context of community detection in real-world networks.

INFORMATION EXCHANGES OF SOCIAL MEDIA EVANGELISTS DURING FLOOD: A SOCIAL NETWORK ANALYSIS

2016 ◽  
Vol 78 (9-3) ◽  
Author(s):  
Abdus-samad Temitope Olanrewaju ◽  
Rahayu Ahmad ◽  
Kamarul Faizal Hashim
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Structure ◽  
Information Dissemination ◽  
Network Structures ◽  
Analysis Technique ◽  
Types Of Information ◽  
Information Brokers

Information dissemination during disaster is very crucial, but inherits several complexities associated with the dynamic characteristics of the disaster. Social media evangelists (activists) play an important role in disseminating critical updates at on-site locations. However, there is limited understanding on the network structure formed and its evolution and the types of information shared. To address these questions, this study employs Social Network Analysis technique on a dataset containing 157 social media posts from an influential civilian fan page during Malaysia’s flood. The finding demonstrates three different network structures emerged during the flood period. The network structure evolves depending on the current state of the flood, the amount of information available and the need of information. Through content analysis, there were seven types of information exchanges discovered. These information exchanges evolved as the scale and magnitude of flood changes. In conclusion, this study shows the emergence of different network structures, density and identification of influential information brokers among civilians that use social media during disaster. Despite the low number of influential information brokers, they successfully manage their specific cluster in conveying information about the disaster and most importantly coordinating the rescue mission.

scholarly journals Personal Network Analysis in the Study of Social Support: The Case of Chronic Pain

2018 ◽  
Vol 15 (12) ◽  
pp. 2695 ◽  
Author(s):  
Rosario Fernández-Peña ◽  
José Molina ◽  
Oliver Valero
Keyword(s):  
Social Support ◽  
Chronic Pain ◽  
Network Analysis ◽  
Network Structure ◽  
Methodological Approach ◽  
Critical Role ◽  
Support Network ◽  
Structured Interviews ◽  
Personal Network ◽  
Support Providers

In the context of chronic illness, the individual’s social and relational environment plays a critical role as it can provide the informal support and care over time, beyond healthcare and social welfare institutions. Social Network Analysis represents an appropriate theoretical and methodological approach to study and understand social support since it provides measures of personal network structure, composition and functional content. The aim of this mixed method study is to present the usefulness of Personal Network Analysis to explore social support in the context of chronic pain. Personal and support network data of 30 people with chronic pain (20 alters for each ego, 600 relationships in total) were collected, obtaining measures of personal network structure and composition as well as information about social support characteristics. Also, semi-structured interviews with participants were conducted to identify the context of their experience of pain, their limitations as regards leading an autonomous life, their social support needs and other aspects concerning the effect of pain on their social and relational lives. This approach shows the importance of non-kin social support providers and the significant role of non-providers in the personal networks of people suffering chronic pain.

Are you passing along something true or false? Dissemination of social media messages about genetically modified organisms

2020 ◽  
pp. 096366252096674
Author(s):  
Qian Xu ◽  
Yunya Song ◽  
Nan Yu ◽  
Shi Chen
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Structural Stability ◽  
Network Structure ◽  
Genetically Modified Organisms ◽  
Genetically Modified ◽  
Network Density ◽  
Media Messages ◽  
Practical Implications

Using network analysis, this study investigates how information veracity and account verification influence the dissemination of information in the context of discourse about genetically modified organisms on social media. We discovered that misinformation and true information about genetically modified organisms demonstrated different dissemination patterns on social media. In general, the dissemination networks of misinformation about genetically modified organisms were found to have higher structural stability than those of true information about genetically modified organisms, as shown by the denser network structure with fewer distinct subgroups residing within the dissemination networks. More importantly, unverified account status significantly boosted the dissemination of misinformation by increasing network density. In addition, we found that the posts about genetically modified organisms from unverified accounts received more reposts and had more layers of information relay than those from the verified accounts. Theoretical and practical implications of these findings on combating misinformation are discussed in the article.

Academics and their online networks: Exploring the role of academic social networking sites

First Monday ◽  
2014 ◽  
Author(s):  
Katy Jordan
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Network Analysis ◽  
Social Networking ◽  
Network Structure ◽  
Social Networking Sites ◽  
Rapid Rise ◽  
Online Networks ◽  
Peripheral Position

The rapid rise in popularity of online social networking has been followed by a slew of services aimed at an academic audience. This project sought to explore network structure in these sites, and to explore trends in network structure by surveying participants about their use of sites and motivations for making connections. Social network analysis revealed that discipline was influential in defining community structure, while academic seniority was linked to the position of nodes within the network. The survey revealed a contradiction between academics use of the sites and their position within the networks the sites foster. Junior academics were found to be more active users of the sites, agreeing to a greater extent with the perceived benefits, yet having fewer connections and occupying a more peripheral position in the network.

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