scholarly journals Exploring Interdisciplinary Relationships Among King Abdulaziz University Departments via ResearchGate: Network Analysis and Visualizations

2020 ◽  
Vol 11 (3) ◽  
pp. 55
Author(s):  
Hanan M. Baaqeel ◽  
Sara F. Aloufi ◽  
Tariq Elyas
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Random Graph ◽  
Degree Distribution ◽  
Small World ◽  
Education Sector ◽  
Graph Models ◽  
Random Graph Models ◽  
King Abdulaziz University ◽  
Academic Social Network

Because all disciplines are connected, interdisciplinary studies are one of the most significant discussions in the education sector. It involves the merging of two or more academic disciplines into one activity. The aim of this research paper is to explore the relationship of interdisciplinary research and network among all departments at King Abdulaziz University (KAU) in ResearchGate (RG) by using the statistical network analysis of undirected social networks. In our academic network, the departments of the university represent the vertices and their academic relationships. We will detect the communities between the departments in RG network by using statistical analysis of the network for each community. Finally, we will compare the academic social network at KAU to some random graph models, and investigate some random graph characteristics, such as power-law, small-world, and scale-free models. In our research, we found that the Department of Chemistry has the highest degree for the academic social network at KAU in RG, and the highest eigenvector centrality as well. In terms of vertex centrality, the Department of Electrical and Computer Engineering has the highest value in closeness and betweenness centrality. Also, we found that the most two connected departments are the Department of Computer Science and Department of Physics through the edge weight equals 248. By using community detection, we found there are seven communities. We conclude that the degree distribution of the academic social network of KAU in RG is different from the degree distribution of random graph models, but it is slightly close to small world model. This study , in turn, can participate to achieve one of the goals of Vision 2030 by shedding some light into how to improve research networks in the education sector and research among Saudi universities.

Μeasuring and Visualizing Coders’ Reliability: New Approaches and Guidelines From Experimental Data

2020 ◽  
pp. 004912412092619
Author(s):  
Iasonas Lamprianou
Keyword(s):  
Experimental Data ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Graph ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
New Approach ◽  
Random Graph Models ◽  
Exponential Random Graph

This study investigates inter- and intracoder reliability, proposing a new approach based on social network analysis (SNA) and exponential random graph models (ERGM). During a recent exit poll, the responses of voters to two open-ended questions were recorded. A coding experiment was conducted where a group of coders coded a sample of text segments. Analyzing the data, we show that the proposed SNA/ERGM method extends significantly our analytical leverage, beyond what popular tools such as Krippendorff’s α and Fleiss’s κ have to offer. The reliability of coding for individual coders differed significantly for the two questions although they were very similar and the same codebook was used. We conclude that the main advantages of the proposed SNA/ERGM method are the intuitive visualizations and the nuanced measurements. Detailed guidelines are provided for practitioners who would like to use the proposed method in operational settings.

scholarly journals Social network analysis of lodging establishments in Tiradentes (MG): Profile and evidence of relationships genesis

2020 ◽  
Vol 22 (3) ◽  
pp. 424-445
Author(s):  
Geraldo Magela Rodrigues De Vasconcelos ◽  
Gustavo Melo-Silva ◽  
Velcimiro Inácio Maia
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Graph ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph

A análise de redes sociais (ARS) constitui um grande avanço na pesquisa em turismo ao revelar as características das relações estabelecidas, apresentando suas estruturas e propriedades. Este trabalho objetivou caracterizar e analisar a rede de cooperação formada entre proprietários de pousadas em Tiradentes-MG. Com o objetivo de explorar o contexto da pesquisa, foram realizadas entrevistas junto a sete proprietários de pousadas. Posteriormente, para a coleta dos dados, foi aplicado um questionário aos proprietários. Por meio da técnica da “bola de neve” foi gerada a rede de cooperação. A partir disso, utilizou-se das técnicas da ARS, com ênfase nas métricas descritivas e pela modelagem de grafos aleatórios da família exponencial (ERGM - Exponential Random Graph Models). Com isso, foi possível caracterizar a rede de cooperação e analisar suas propriedades. A rede possui 54 pousadas, com uma densidade geral baixa, pois há apenas 4,7% de relações possíveis. Foram identificadas 17 pousadas centrais e 37 periféricas. No subgrupo central, a densidade foi de 15,4% e no subgrupo periférico, de apenas 2,6%. A rede de cooperação observada apresentou homofilia por gênero e por procedência dos proprietários. Os resultados da modelagem ERGM permitiram explicações probabilísticas em termos de atributos endógenos dos proprietários, como gênero e procedência.

scholarly journals The performance of permutations and exponential random graph models when analyzing animal networks

2020 ◽  
Vol 31 (5) ◽  
pp. 1266-1276 ◽  
Author(s):  
Julian C Evans ◽  
David N Fisher ◽  
Matthew J Silk
Keyword(s):  
Social Network ◽  
Random Graph ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Network Data ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph

Abstract Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.

scholarly journals Inferential Network Analysis with Exponential Random Graph Models

2011 ◽  
Vol 19 (1) ◽  
pp. 66-86 ◽  
Author(s):  
Skyler J. Cranmer ◽  
Bruce A. Desmarais
Keyword(s):  
Network Analysis ◽  
Random Graph ◽  
Graph Model ◽  
Network Data ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Model ◽  
General Acceptance ◽  
Random Graph Models ◽  
Exponential Random Graph

Methods for descriptive network analysis have reached statistical maturity and general acceptance across the social sciences in recent years. However, methods for statistical inference with network data remain fledgling by comparison. We introduce and evaluate a general model for inference with network data, the Exponential Random Graph Model (ERGM) and several of its recent extensions. The ERGM simultaneously allows both inference on covariates and for arbitrarily complex network structures to be modeled. Our contributions are three-fold: beyond introducing the ERGM and discussing its limitations, we discuss extensions to the model that allow for the analysis of non-binary and longitudinally observed networks and show through applications that network-based inference can improve our understanding of political phenomena.

scholarly journals Greed is Good for Deterministic Scale-Free Networks

Algorithmica ◽  
2020 ◽  
Vol 82 (11) ◽  
pp. 3338-3389
Author(s):  
Ankit Chauhan ◽  
Tobias Friedrich ◽  
Ralf Rothenberger
Keyword(s):  
Random Graph ◽  
Random Graphs ◽  
Power Law ◽  
Real World ◽  
Degree Distribution ◽  
High Probability ◽  
Maximum Independent Set ◽  
Graph Models ◽  
Random Graph Models ◽  
Graph Classes

Abstract Large real-world networks typically follow a power-law degree distribution. To study such networks, numerous random graph models have been proposed. However, real-world networks are not drawn at random. Therefore, Brach et al. (27th symposium on discrete algorithms (SODA), pp 1306–1325, 2016) introduced two natural deterministic conditions: (1) a power-law upper bound on the degree distribution (PLB-U) and (2) power-law neighborhoods, that is, the degree distribution of neighbors of each vertex is also upper bounded by a power law (PLB-N). They showed that many real-world networks satisfy both properties and exploit them to design faster algorithms for a number of classical graph problems. We complement their work by showing that some well-studied random graph models exhibit both of the mentioned PLB properties. PLB-U and PLB-N hold with high probability for Chung–Lu Random Graphs and Geometric Inhomogeneous Random Graphs and almost surely for Hyperbolic Random Graphs. As a consequence, all results of Brach et al. also hold with high probability or almost surely for those random graph classes. In the second part we study three classical $$\textsf {NP}$$ NP -hard optimization problems on PLB networks. It is known that on general graphs with maximum degree $$\Delta$$ Δ , a greedy algorithm, which chooses nodes in the order of their degree, only achieves a $$\Omega (\ln \Delta )$$ Ω ( ln Δ ) -approximation for Minimum Vertex Cover and Minimum Dominating Set, and a $$\Omega (\Delta )$$ Ω ( Δ ) -approximation for Maximum Independent Set. We prove that the PLB-U property with $$\beta >2$$ β > 2 suffices for the greedy approach to achieve a constant-factor approximation for all three problems. We also show that these problems are -hard even if PLB-U, PLB-N, and an additional power-law lower bound on the degree distribution hold. Hence, a PTAS cannot be expected unless = . Furthermore, we prove that all three problems are in if the PLB-U property holds.

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