An analysis of the structure and evolution of the scientific collaboration network of computer intelligence in games

Lara-Cabrera, Raúl; Cotta, Carlos; Fernández-Leiva, Antonio J.

doi:10.1016/j.physa.2013.10.036

Cited by 18 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The structure of scientific collaboration is often analyzed by drawing on proxy measures such as co-authorship networks. A co-authorship network is comprised of researchers as vertices and joint publications as edges [1,2,3,4,5,6,7,8]. Modeling the structure of co-authorship networks can be instrumental for designing better higher education and research institutions, mapping knowledge domains [9,10,11,12], understanding how innovation comes about [13,14], and understanding in how far the network structure has repercussions on individual researchers' performance [15,16,2].…”

Section: Collaboration and Polarization In The Social Sciencesmentioning

confidence: 99%

Polarization in the social sciences: Assortative mixing in social science collaboration networks is resilient to interventions

Leifeld

2018

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

Academic collaboration in the social sciences is characterized by a polarization between hermeneutic and nomological researchers. This polarization is expressed in different publication strategies. The present article analyzes the complete co-authorship networks in a social science discipline in two separate countries over five years using an exponential random graph model. It examines whether and how assortative mixing in publication strategies is present and leads to a polarization in scientific collaboration. In the empirical analysis, assortative mixing is found to play a role in shaping the topology of the network and significantly explains collaboration. Co-authorship edges are more prevalent within each of the groups, but this mixing pattern does not fully account for the extent of polarization. Instead, a thought experiment reveals that other components of the complex system dampen or amplify polarization in the data-generating process and that microscopic interventions targeting behavior change with regard to assortativity would be hindered by the resilience of the system. The resilience to interventions is quantified in a series of simulations on the effect of microscopic behavior on macroscopic polarization. The empirical study controls for geographic proximity, supervision, and topical similarity (using a vector space model), and the interplay of these factors is likely responsible for this resilience. The paper also predicts the co-authorship network in one country based on the model of collaborations in the other country.

show abstract

Section: Collaboration and Polarization In The Social Sciencesmentioning

confidence: 99%

Polarization in the social sciences: Assortative mixing in social science collaboration networks is resilient to interventions

Leifeld

2018

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

show abstract

“…Complex networks can well characterize the internal relationship between research objects(nodes) [12] , and therefore, have been widely used in many elds in recent years [13][14][15][16][17][18] . Most complex networks are scale-free, and a small number of hub nodes play a leading role in the operation of the network [19] .…”

Section: Introductionmentioning

confidence: 99%

Propagation Network of Tailings Dam Failure Risk: An Empirical Research and The Identification of Key Hazard Node

Zhen¹,

Ma²,

Zhao³

2021

Preprint

View full text Add to dashboard Cite

The tailings dam system is complex, and the dam structure changes continuously over time, which makes it difficult to identify hazards and analyze the causes of failure accidents. This paper uses hazards to represent the nodes, and the relationship between hazards to represent the edges. Based on the complex network theory, the propagation network of tailings dam failure risk is constructed. The traditional identification methods usually focus on one aspect of the information of the network, while it cannot take into account to absorb the advantages of different methods, resulting in the lack of information, which will lead to a certain difference between identified key hazards and real key hazards. In order to solve this problem, by absorbing the advantages of different methods under different hazard remediation (deleted) ratios, combined with the characteristics of multi-stage propagation of tailings dam failure risk, this paper proposes a multi-stage collaborative hazard remediation method (MCHRM) to determine the importance of hazard nodes. When the important nodes of this network that affect the network efficiency are found, by consulting the monitoring data, daily inspection results and safety evaluation information of each hazard before the dam failure, we can determine the real cause of the accident from the above important nodes according to the grading standards of hazard indicators. In the application example of Feijão Dam I, this article compares the key hazards obtained by the above methods with the conclusions of the accident investigation team. It can be found that the above method has a very good effect on finding the key causes of tailings dam failure.

show abstract

“…SNA is applied to show the evolution of collaboration network and collaboration mode Çavuşoğlu and Türker (2014). As an important part of SNA, the centrality analysis is used to illustrate the characteristics of actors in the network from a micro perspective (Lara-Cabrera et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

The Analysis of the Characteristics and Evolution of the Collaboration Network in Blockchain Domain

Yu¹,

Chen²

2020

Informatica

View full text Add to dashboard Cite

Blockchain is a decentralized database, which can protect the safety of trade and avoid double payment. Due to the widespread attention of researchers, the studies of this field have increased sharply in recent years. It is meaningful to reveal the development level and trends based on this literature. This paper adopts bibliometric methods to study the collaboration characteristics from the levels of author, institution and country. Furthermore, several kinds of collaboration networks and their centrality analysis are also presented, which not only display the development level and collaboration degree but also the evolution of author collaboration modes in different phases.

show abstract

An analysis of the structure and evolution of the scientific collaboration network of computer intelligence in games

Cited by 18 publications

References 28 publications

Polarization in the social sciences: Assortative mixing in social science collaboration networks is resilient to interventions

Polarization in the social sciences: Assortative mixing in social science collaboration networks is resilient to interventions

Propagation Network of Tailings Dam Failure Risk: An Empirical Research and The Identification of Key Hazard Node

The Analysis of the Characteristics and Evolution of the Collaboration Network in Blockchain Domain

Contact Info

Product

Resources

About