Ego Network Analysis Research Articles

Between Learning and Opportunity: A Study of African American Coders’ Networks of Support

This study examines how African American adults attending a code bootcamp continue to learn coding literacy despite life challenges associated with racial oppression. Eleven out of twelve study participants drew maps of their support and discussed in one-on-one interviews how the people, objects, and animals in their drawings assisted their approaching learning computer programming. Applying ego network analysis, these interviews and drawings suggest that participants use various clusters of support in their network to provide the personal resources coders need to code and what is hard to come by in situations of racial injustice. These resources may have helped participants manage the risks of losing access to coding literacy. Instead of a universal approach to accessing technology, different kinds of networks and resources can lead to continuous access. This study furthers research on racially marginalized adults’ digital literacies and demonstrates how ego network analysis maybe useful for qualitative research on theories of ecological writing.

The main factors affecting cultural exchange between Korea and China: A semantic network analysis based on the cultural governance perspective

The cultural exchange paradigm evolved into cultural governance because of recent changes in the cultural exchange environment. This resulted in the diversification of cultural exchange subjects, the multi-channelization of methods, and diversification of contents; furthermore, cooperation between government and private sector has become particularly important in the cultural exchange process. This study examines influential factors in the cultural exchange between Korea and China from the perspective of cultural governance. To accomplish this, we collect text data on the cultural exchange between the two countries and conduct text mining. We then perform semantic network analysis and ego network analysis by selecting 80 major words included in the data collected from the digital portals of both countries. The results show that cultural exchange between Korea and China has transformed from government-led initiatives into private sector ones with information, communication, and technology-based platforms as important channels. For example, in the Korean channel, the “Korean Wave” played a central role in cultural exchange, whereas in the Chinese channel, the capacities of civilians and government agencies were the main factors. Moreover, cultural exchange between the two countries has been expanding, thus indicating the increasing importance of civilians and private platforms in cultural governance.

An ego network analysis approach identified important biomarkers with an association to progression and metastasis of gastric cancer.

Gastric cancer (GC) is the fifth most common cancer type worldwide. The aim of this study was to identify gastric-related therapeutic indicators on the basis of the ego network analysis. The microarray data related to GC was downloaded from ArrayExpress database. All human protein-protein interaction (PPI) networks were downloaded from the STRING database. Ego genes were identified on the basis of PPI networks and the gene expression in GC, and then co-expression networks (ego networks) were constructed using these ego genes. On the basis of ego networks, the optimal GO terms and genes were predicted by affinity predictions and cold read predictions. Finally, the predicted genes as effective biomarkers for GC were verified by the bioinformatics analysis. The differential expression networks were conducted and comprised of 365 edges and 232 nodes, which resulted in 218 ego genes. Although there was no significant difference in the expression of top ten ego genes among different groups of GC samples, it was eventually confirmed that top three optimal GO terms with highest cool read values were translational termination (cool read value = 0.987), translational elongation (cool read value = 0.986), and macromolecular complex disassembly (cool read value = 0.985) and top five optimal genes were UBA52, RPS27A, MAPK1, UBC, and UBB. UBA52, RPS27A, and MAPK1 were verified by the bioinformatics analysis to be related to the progression and metastasis of GC. An ego network analysis approach is a very effective method for screening GC and the screened genes might be biomarkers for GC diagnosis and treatment.

From Complex Interventions to Complex Systems: Using Social Network Analysis to Understand School Engagement with Health and Wellbeing.

Challenges in changing school system functioning to orient them towards health are commonly underestimated. Understanding the social interactions of school staff from a complex systems perspective may provide valuable insight into how system dynamics may impede or facilitate the promotion of health and wellbeing. Ego social network analysis was employed with wellbeing leads within four diverse case study schools to identify variability in embeddedness of health and wellbeing roles. This variation, as well as the broader context, was then explored through semi-structured qualitative interviews with school staff and a Healthy Schools Coordinator, sampled from the wellbeing leads’ ego-networks. Networks varied in terms of perceived importance and frequency of interactions, centrality, brokerage and cliques. Case study schools that showed higher engagement with health and wellbeing had highly organised, distributed leadership structures, dedicated wellbeing roles, senior leadership support and outside agencies embedded within school systems. Allocation of responsibility for wellbeing to a member of the senior leadership team alongside a distributed leadership approach may facilitate the reorientation of school systems towards health and wellbeing. Ego-network analysis to understand variance in complex school system starting points could be replicated on a larger scale and utilised to design complex interventions.

Networked individualism and learning in organizations

PurposeThis paper aims to investigate the extent professionals from the vocational sector are networked individuals. The authors explore how professionals use their personal networks to engage in a wide variety of learning activities and examine what social mechanisms influence professionals’ agency to form personal informal learning networks.Design/methodology/approachThis study applied a mixed-method approach to data collection. Social network data were gathered among school professionals working in the vocational sector. Ego-network analysis was performed. A total of 24 in-depth, semi-structured, qualitative interviews were analyzed.FindingsThis study found that networked individualism is not represented to its full potential in the vocational sector. However, it is important to form informal learning ties with different stakeholders because all types of informal learning ties serve different learning purposes. The extent to which social mechanisms (i.e. proximity, trust, level of expertise and homophily) influence professionals’ agency to form informal learning ties differs depending on the stakeholder with whom the informal learning ties are formed.Research limitations/implicationsThis study excludes the investigation of social mechanisms that shape learning through more impersonal virtual learning resources, such as social media or expert forums. Moreover, the authors only included individual- and dyadic-level social mechanisms.Practical implicationsBy investigating the social mechanisms that shape informal learning ties, this study provides insights how professionals can be stimulated to build rich personal learning networks in the vocational sector.Originality/valueThe authors extend earlier research with in-depth information on the different types of learning activities professionals engage in in their personal learning networks with different stakeholders. The ego-network perspective reveals how different social mechanisms influence professionals’ agency to shape informal learning networks with different stakeholders.

Homophily in co-autorship networks

Abstract The main purpose of this paper is to measure the impact that homophily, structural characteristics of the networks, number of citations of the alters and their Hirsch score have on the number of citations of an ego. I have chosen co-authorship networks as a subject of research because they have a great influence on knowledge and on the diffusion of ideas. The studied populations are represented by full-time academics affiliated to sociology departments in Romania, Poland and Slovenia. Ego-network analysis was used as research design. The data was analyzed using linear hierarchical regression. For all three populations the average number of citations of the alter has a considerable positive impact on the number of citations of the ego. Conversely, the Hirsch score of the alter has a negative impact on the number of citations of the ego. The data analyzed in this article claims that the assumptions about the positive impact of alter citations, network size and the betweenness score on the number of the authors citations are supported empirically.

Combining value chain and social network analysis as a viable tool for informing targeted disease surveillance in the rural poultry sector of Zambia.

Diseases are among the greatest challenges to the rural poultry sector in sub-Saharan Africa. The lack of a sustainable poultry disease surveillance system and the possible existence of communities and occasions where the interaction between birds is high present an opportunity for targeted surveillance of poultry diseases in these regions. However, the establishment of such a system requires adequate knowledge of the sector in the targeted area. Zambia is an example of a developing country located in the tropics that faces the challenge of frequent poultry disease outbreaks. Consequently, an interview-based survey to study the poultry sector's market chain and social networks was conducted in Eastern Zambia to derive information required for configuring targeted surveillance. This survey involved a poultry value chain analysis that also included an assessment of trading practices to identify biosecurity hot spots within the chain that could be targeted for disease surveillance. A social network analysis of poultry movement within Eastern Zambia was also conducted using whole-network analysis and ego network analysis to identify poultry trade hubs that could be targeted for poultry disease surveillance based on their centrality within the network and their size and influence within their ego networks. Rural farmers, middlemen and market traders were identified as biosecurity risk hot spots whose poultry and utensils could be targeted for disease surveillance within the value chain. Furthermore, social network analysis identified four districts as poultry trade hubs that could be targeted for disease surveillance. This study is the first to formally describe poultry movement networks within Zambia and the surrounding region. Its findings provide data required to implement targeted surveillance in regions where resources are either inadequate or non-existent, and the results provide a deeper understanding of the cultural and practical constraints that influence trade in developing countries.

토픽모델링과 에고 네트워크 분석을 활용한 스마트 헬스케어 연구동향 분석

스마트 헬스케어는 ICT 분야와 의료서비스 분야가 융·복합 된 분야로 다양한 분야에서 학제 간 융·복합 연구가 활발히 이루어지고 있다. 본 연구는 토픽모델링(Topic Modeling)과 에고 네트워크 분석(Ego Network Analysis)을 활용하여 스마트 헬스케어 연구동향을 살피는데 그 목적이 있다. 이를 위해 2001년부터 2018년 4월까지 Scopus에 게재된 2,690편을 대상으로 텍스트 분석, 각 기간별 빈도분석, 토픽모델링, 워드 클라우드, 에고 네트워크 분석을 수행하였다. 토픽 모델링 분석 결과 8개의 주요 연구토픽이 도출되었다. 8개 주요 연구토픽은 “AI in healthcare”, “ Smart hospital”, “Healthcare platform”, “ blockchain in healthcare”, “Smart health data”, “Mobile healthcare”, “Wellness care”, “Cognitive healthcare” 순으로 나타났다. 토픽모델링 결과를 보다 심도 있게 살펴보기 위해 연구토픽별 에고 네트워크 분석을 하였다. 이를 통해 스마트 헬스케어 연구동향을 파악하고, 향후 연구의 방향성을 수립하는데 시사점을 제시하고자 한다.

High-Level Programming Abstractions for Distributed Graph Processing

Efficient processing of large-scale graphs in distributed environments has been an increasingly popular topic of research in recent years. Inter-connected data that can be modeled as graphs arise in application domains such as machine learning, recommendation, web search, and social network analysis. Writing distributed graph applications is inherently hard and requires programming models that can cover a diverse set of problem domains, including iterative refinement algorithms, graph transformations, graph aggregations, pattern matching, ego-network analysis, and graph traversals. Several high-level programming abstractions have been proposed and adopted by distributed graph processing systems and big data platforms. Even though significant work has been done to experimentally compare distributed graph processing frameworks, no qualitative study and comparison of graph programming abstractions has been conducted yet. In this survey, we review and analyze the most prevalent high-level programming models for distributed graph processing, in terms of their semantics and applicability. We identify the classes of graph applications that can be naturally expressed by each abstraction and we also give examples of applications that are hard or impossible to express. We review 34 distributed graph processing systems with respect to their programming abstractions, execution models, and communication mechanisms. Finally, we discuss trends and open research questions in the area of distributed graph processing.

Mapping the hierarchical structure of the global shipping network by weighted ego network analysis

An improved complex network approach named weighted ego network analysis (WENA) was employed to explore the hierarchical structure of the global shipping network. First, it was found that all centrality indices exhibited scale-free properties with obvious power-law distributions. That is, less than 30% of shipping ports had quite high centrality values, which are powerful hubs of the global shipping system. Those very accessible shipping ports showed significant preferential attachment properties such as 'rich-club' and 'Matthew' effect. Second, results of WENA revealed its core-peripheral hierarchical structure, which could be divided into five levels of sub-networks: first tier (top 5%), second tier (top 5%~15%), third tier (top 15%~30%), fourth tier (top 30%~50%), and fifth tier (the remainder). Finally, five kinds of correlation analyses have been carried out, including degree vs. degree, degree vs. strength, degree vs. clustering coefficient, degree vs. distance and centrality vs. GDP. Results indicated that the connectivity of a shipping port was both significantly influenced by the connectivity of its directly connected ports, the shipping distance and hinterland economic scale.

Mapping the hierarchical structure of the global shipping network by weighted ego network analysis

An improved complex network approach named weighted ego network analysis (WENA) was employed to explore the hierarchical structure of the global shipping network. First, it was found that all centrality indices exhibited scale-free properties with obvious power-law distributions. That is, less than 30% of shipping ports had quite high centrality values, which are powerful hubs of the global shipping system. Those very accessible shipping ports showed significant preferential attachment properties such as 'rich-club' and 'Matthew' effect. Second, results of WENA revealed its core-peripheral hierarchical structure, which could be divided into five levels of sub-networks: first tier (top 5%), second tier (top 5%~15%), third tier (top 15%~30%), fourth tier (top 30%~50%), and fifth tier (the remainder). Finally, five kinds of correlation analyses have been carried out, including degree vs. degree, degree vs. strength, degree vs. clustering coefficient, degree vs. distance and centrality vs. GDP. Results indicated that the connectivity of a shipping port was both significantly influenced by the connectivity of its directly connected ports, the shipping distance and hinterland economic scale.

Tourist destination network analysis: The ego network role

This paper aims to analyse the different roles that enterprises have within a tourist destination by identifying the presence and possible role of leaders within the system. The Social Network Analysis (SNA) is a tool that offers a greater degree of understanding of the operation of the destination. The map of commercial relations between the leading players of tourist supply can provide greater insight into the main relations existing between enterprises and the principles that ensure and regulate operation. In keeping with this objective and building on the results of a previous paper (Iannolino and Ruggieri, 2012), the authors have focused their attention on the role of some enterprises operating in San Vito Lo Capo (Italy) to determine the extent of their aggregating force vis-à-vis the destination. The ego-network analysis (ENA) has been applied to the existing relations between the enterprises at the destination San Vito Lo Capo, to determine the presence or absence of groups of enterprises, called Egos, which, with respect to the others, play an important role or are to be considered key subjects for the entire system in commercial terms. Following on this first results, the paper explores ways in which enterprises belonging to the ego networks are the key players responsible for a better climate of cooperation and trust among all of the system's enterprises.

Ego Network Analysis for Predicting Students Performance

Ego Network Analysis for Predicting Students Performance

The Research Trend and the Knowledge Structure in Rental Housing Studies

The housing policy of South Korea has changed to focus on the rental housing, research topics on rental housing are also making a change. This study verified the research trends regarding rental housing in recent 10 years by network analysis. This study analysed networks among 1,372 guide words of 490 studies, and it shows that the words connected each other within 4.48 steps on average. This means studies on rental housing have ‘narrow structure’ than other fields of study. At the keyword frequency analysis, ‘living satisfaction’ and ‘housing policy’ show high frequency. However, at the centrality analysis, ‘housing welfare’ is located in the center connecting otherkey words. At Ego-Network Analysis, the studies on low income group supporting system establish networks.

Spatial, network and temporal dimensions of the determinants of adaptive capacity in poor urban areas

Abstract Slums and informal settlements are home to rapidly growing populations in urban areas globally and face a range of significant shocks and stresses. The sustainability of these places is critically intertwined with the resilience of their populations. The nature of the capacity for populations to adapt to shocks, as an element of resilience, is related to the evolving knowledge and networks of those populations and is suggested here to have significant spatial and temporal variation. We analyse the key determinants of adaptive capacity and hypothesise that they are related to spatial dimensions of urban form, temporal dimensions of migration, place attachment, and to social differentiation. We investigate these dynamics of adaptive capacity across a transect of urbanisation from inner city to periphery in Kampala, Uganda using diverse methods including a sample survey of residents (n = 720) and ego-network analysis. Results show that the key determinants of individual-level adaptive capacity are attachment to place, social networks, and duration of residence. There are significant differences in adaptive capacity between slum areas, as well as strong social group and temporal dimensions. These findings suggest the importance of measuring adaptive capacities at appropriate spatial and temporal scales in order to identify specific interventions for slums that build the resilience of their populations.

The Power of Brokerage: Case Study of Normative Behavior, Latinas and Cervical Cancer

Informed by the theory of normative social behavior (TNSB) and ego-network analysis, the present study outlines the mechanisms that contribute to the creation and maintenance of social norms and th...

Spatial heterogeneity of ports in the global maritime network detected by weighted ego network analysis

ABSTRACTMore extensive attention has been paid to the heterogeneity of maritime transport network in topological rather than in spatial aspects. However, the importance of links and the roles of neighbors of a node has been ignored if not all. To fill this gap, this article introduced the approach of weighted ego network analysis (WENA) to visualize the spatial heterogeneity of the maritime network at global and local levels. The topological connectivity graph of the global marine network was derived, and its structural properties were analyzed. It is found out that the values of the degree of ports follow power-law distribution, which indicates that the global marine network is scale-free, that is, there are few well-connected ports and a majority of less connected ports. The spatial disparities of the network can be described by a core–periphery pattern. In global, most of the hubs or ports with extremely high values of degree locate in the big-three maritime regions including Far East, North America, and West Europe. Along the peripheral belts of the three regions, there are lots of less connected small ports. A different hierarchical structure of six continents was captured by WENA. It is found that Europe, Asia, North America, and Africa showcase a pyramid-shaped hierarchical structure with a scale-free feature similar to the entire network, while South America and Oceania exhibit the fusiform hierarchy like small-world networks. It is proposed that such spatial inequality and heterogeneity were caused by the geographical environments such as the hub-and-spoke organization, the embedded trade pattern, and the proximity of location. These findings help us to understand the characteristics of the international trade pattern and shed light on the strategies of development for the industry stakeholders.

Analysis of Swedish Forest Owners' Information and Knowledge-Sharing Networks for Decision-Making: Insights for Climate Change Communication and Adaptation.

To further the understanding of climate change adaptation processes, more attention needs to be paid to the various contextual factors that shape whether and how climate-related knowledge and information is received and acted upon by actors involved. This study sets out to examine the characteristics of forest owners' in Sweden, the information and knowledge-sharing networks they draw upon for decision-making, and their perceptions of climate risks, their forests' resilience, the need for adaptation, and perceived adaptive capacity. By applying the concept of ego-network analysis, the empirical data was generated by a quantitative survey distributed to 3000 private forest owners' in Sweden in 2014 with a response rate of 31%. The results show that there is a positive correlation, even though it is generally weak, between forest owner climate perceptions and (i) network features, i.e. network size and heterogeneity, and (ii) presence of certain alter groups (i.e. network members or actors). Results indicate that forest owners' social networks currently serve only a minimal function of sharing knowledge of climate change and adaptation. Moreover, considering the fairly infrequent contact between respondents and alter groups, the timing of knowledge sharing is important. In conclusion we suggest those actors that forest owners' most frequently communicate with, especially forestry experts providing advisory services (e.g. forest owner associations, companies, and authorities) have a clear role to communicate both the risks of climate change and opportunities for adaptation. Peers are valuable in connecting information about climate risks and adaptation to the actual forest property.

Effect of Comt1 genetype on anxiety and nociceptive sensitivity: An ego network analysis approach

PurposeThe aim of this study was to predict key genes and their relationships for anxiety and nociceptive sensitivity related to Comt1 genetype.MethodsThe raw data of E-GEOD-20160 related to anxiety and nociceptive sensitivity were obtained. Pearson correlation coefficient of interaction in protein–protein interaction was calculated. Topological analysis was processed for protein–protein interaction network, and genes in this network were ranked based on their degrees. Ego genes were identified, and models were searched and refined. A total of 1000 randomized tests were processed for ego networks. The classification accuracy of each ego network was obtained in this process.ResultsThe interactions with genes in gene expression profiles were extracted, and protein–protein interaction was constructed. The protein–protein interaction included 4639 genes and 43,837 relationships. Differential co-expression network was constructed, and 74 ego genes were obtained. Thereinto, top five ego genes were ADCY2, GRM8, S1PR3, ADCY6, and ANXA1. After module searching and refinement, a total of 11 candidate modules were obtained, including module 14, module 51, and module 9. In addition, these 11 modules were confirmed to be with significance. Module 14 contained 10 genes, such as HRH3, DRD2, and CXCR3. Similarly, module 51 included six genes, such as HELZ2, NCOA3, and MED30.ConclusionsEgo network analysis was a useful and comprehensive method for biomarkers screening. Several modules such as module 3 and module 36 were important subnetworks. Potential genes in these modules including ADCYs, GNAI1, DRD2, PNOC, CCR2, DRD2, and LPAR1 might be important genes in the research of anxiety and nociceptive sensitivity.

EgoComp: A node-link-based technique for visual comparison of ego-networks

Analysis of ego-networks is a critical research problem when analyzing large-scale social networks, as an ego-network represents the social circle a person actually contacts with. One of the core tasks in ego-network analysis is visual comparison, which includes edge comparison and node comparison. Although various works have been done to support comparing normal networks and ego-networks, intuitive node comparison of two ego-networks is still challenging. In this article, we propose egoComp, an intuitive and expressive visualization technique, to analyze the node difference between two ego-networks. To preserve the latent structure of ego-network and lay emphasis on intuitiveness, our design is node-link-based (radial tree layout) and uses a side-by-side method to compare ego-networks. We design a novel storyflow-like graph layout to reveal the relationship of two ego-networks at the individual node level. Furthermore, three different layout algorithms, including origin, greedy, and assignment algorithms, are proposed to meet different user requirements. We demonstrate the effectiveness of our system through case studies and a user study and then discuss the limitations thoroughly as well as the possible solutions and potential future work.