Evolutionary stable strategies in networked games: the influence of topology

Evolutionary game theory is used to model the evolution of competing strategies in a population of players. Evolutionary stability of a strategy is a dynamic equilibrium, in which any competing mutated strategy would be wiped out from a population. If a strategy is weak evolutionarily stable, the competing strategy may manage to survive within the network. Understanding the network-related factors that affect the evolutionary stability of a strategy would be critical in making accurate predictions about the behaviour of a strategy in a real-world strategic decision making environment. In this work, we evaluate the effect of network topology on the evolutionary stability of a strategy. We focus on two well-known strategies known as the Zero-determinant strategy and the Pavlov strategy. Zero-determinant strategies have been shown to be evolutionarily unstable in a well-mixed population of players. We identify that the Zero-determinant strategy may survive, and may even dominate in a population of players connected through a non-homogeneous network. We introduce the concept of `topological stability' to denote this phenomenon. We argue that not only the network topology, but also the evolutionary process applied and the initial distribution of strategies are critical in determining the evolutionary stability of strategies. Further, we observe that topological stability could affect other well-known strategies as well, such as the general cooperator strategy and the cooperator strategy. Our observations suggest that the variation of evolutionary stability due to topological stability of strategies may be more prevalent in the social context of strategic evolution, in comparison to the biological context

A parameterised model for link prediction using node centrality and similarity measure based on graph embedding

Link prediction is a key aspect of graph machine learning, with applications as diverse as disease prediction, social network recommendations, and drug discovery. It involves predicting new links that may form between network nodes. Despite the clear importance of link prediction, existing models have significant shortcomings. Graph Convolutional Networks, for instance, have been proven to be highly efficient for link prediction on a variety of datasets. However, they encounter severe limitations when applied to short-path networks and ego networks, resulting in poor performance. This presents a critical problem space that this work aims to address. In this paper, we present the Node Centrality and Similarity Based Parameterised Model (NCSM), a novel method for link prediction tasks. NCSM uniquely integrates node centrality and similarity measures as edge features in a customised Graph Neural Network (GNN) layer, effectively leveraging the topological information of large networks. This model represents the first parameterised GNN-based link prediction model that considers topological information. The proposed model was evaluated on five benchmark graph datasets, each comprising thousands of nodes and edges. Experimental results highlight NCSM's superiority over existing state-of-the-art models like Graph Convolutional Networks and Variational Graph Autoencoder, as it outperforms them across various metrics and datasets. This exceptional performance can be attributed to NCSM's innovative integration of node centrality, similarity measures, and its efficient use of topological information

UNDERSTANDING COMMUNICATION NETWORK COHESIVENESS DURING ORGANIZATIONAL CRISIS: EFFECTS OF CLIQUE AND TRANSITIVITY

Various terms such as organizational mortality, organizational death, bankruptcy, decline, retrenchment and failure have been used in the literature to characterize different forms and facets of organizational crisis. Communication network studies have typically focused on nodes (individuals or organizations), relationships between those nodes, and subsequent affects of these relationships upon the network as a whole. Email networks in contemporary organizations are fairly representative of the underlying communications networks. We show that changes in communication networks and its associated group cohesiveness have implications for studying organizational crisis. In this paper, we analyze the changing communication network structure at Enron Corporation during the period of its crisis (2000-2001). Our goal was to understand how communication patterns and structure were affected by organizational crisis. Drawing on communication network crisis and group cohesiveness theory, we tested several propositions using the Enron email corpus: (1) Number of cliques increases, and (2) Communication network becomes increasingly transitive as organizations experience crisis. The results of the tests and their implications are discussed in this paper

Planning, implementation and effectiveness in Indigenous health reform

The Planning, Implementation and Effectiveness in Indigenous Health Reform (PIE) project, funded by the Lowitja Institute and the Australian Research Council, carried out by the University of Melbourne, arose from concerns by Aboriginal and Torres Strait Islander people that despite the importance of participation and investment in collaborative governance, little research focused on capturing current practice and identifying best practice is being done. The advent of the National Indigenous Reform Agreement (NIRA) and the Indigenous Health National Partnership Agreements (IHNPAs) has led to further development/application of collaborative approaches to governance through committees and forums at national, State and regional levels. The activities associated with these committees and forums are referred to throughout this report as collaborative governance. This report focuses on building the evidence base around best practice based on case studies of collaborative governance in relation to the NIRA. A policy brief highlighting the policy recommendations of this report is also available

Private management and governance styles in a Japanese public hospital: A story of west meets east

This paper examines a case of healthcare governance reform in a Japanese hospital to demonstrate how and why physicians may resist NPM ideals in healthcare. We find that the governance reform departed significantly from its idealized form. The intended structure of decentralized governance was ruptured by the CEO, with unanticipated consequences. The power of the medical school, arising out of the ikyoku system in the context of chronic shortages of physicians and the respect afforded to physicians by wider society, was played out in the hospital, with cost management taking a back seat. We find that the general patterns of interaction between and among key stakeholders in relation to accountability and the governance process are shaped by some form of verticality, monologues rather than dialogues, indirectness and silence rooted in Japanese cultural context. Cultural political economy approach guided us to examine both semiotic and extra semiotic features and their dialectical moments with key actors in assessing the limits of NPMs in non-Western contexts

In-vitro antimicrobial activity of methanolic extract of Ficus racemosa Linn. fruits

Abstract The antimicrobial activity methanol extracts of Ficus racemosa Linn., belonging to the family Moringaceae, was determined in vitro, using disc diffusion method against human pathogenic bacteria fungi. The displayed a potential antibacterial activity against all the tested four Gram negative and Gram positive bacteria: Staphylococcus aureus, Bacillus subtilis, Vibrio cholera, Bacillus cereus, Salmonella typhi, Shigella dysenteriae, Pseudomonas aeruginosa, Klebsiella species and Proteus species as well four fungi: Alternaria spp., Colletotrichum spp., Curvularia spp. and Fusarium spp. The highest zone of inhibition was found in the concentration of 200 µg/disc for Staphylococcus aureus (18mm) and in the concentration of 150 µg/disc for Fusarium spp. (12mm). The consequences of this investigation suggest that the extracts of Ficus racemosa can be used to discover antibacterial agent for developing new pharmaceuticals to control studied human pathogenic bacteria responsible for severe illness

Evolutionary Dynamics of Scientific Collaboration Networks: Multi-Levels and Cross-time Analysis

Several studies exist which use scientific literature for comparing scientific activities (e.g., productivity, and collaboration). In this study, using co-authorship data over the last 40 years, we present the evolutionary dynamics of multi level (i.e., individual, institutional and national) collaboration networks for exploring the emergence of collaborations in the research field of "steel structures". The collaboration network of scientists in the field has been analyzed using author affiliations extracted from Scopus between 1970 and 2009. We have studied collaboration distribution networks at the micro-, meso- and macro-levels for the 40 years. We compared and analyzed a number of properties of these networks (i.e., density, centrality measures, the giant component and clustering coefficient) for presenting a longitudinal analysis and statistical validation of the evolutionary dynamics of "steel structures" collaboration networks. At all levels, the scientific collaborations network structures were central considering the closeness centralization while betweenness and degree centralization were much lower. In general networks density, connectedness, centralization and clustering coefficient were highest in marco-level and decreasing as the network size grow to the lowest in micro-level. We also find that the average distance between countries about two and institutes five and for authors eight meaning that only about eight steps are necessary to get from one randomly chosen author to another.Comment: Accepted for publication in Scientometric

The impact of study load on the dynamics of longitudinal email communications among students

With the advent of information technology, emails have gained wide acceptability among students as an asynchronous communication tool. According to the current pedagogy literature the overall trend of the use of email communication by university students has been increasing significantly since its inception, despite the rapid growth of the popularity and acceptability of other social mediums (e.g. Mobile phone and Facebook). In this study, we explore a longitudinal email communication network, which evolved under an increasing study load among 38 students throughout a university semester, using measures of social network analysis (SNA) and exponential random graph (ERG) models. This longitudinal network was divided into three waves, where each wave represents the portion of the complete longitudinal network that evolves between two consecutive observations. An increased study load was imposed through the assessment components of the course. SNA measures of degree centrality (i.e. the activity of an actor or actor popularity), betweenness centrality (i.e. the capacity to control the flow of information in a network), closeness centrality (i.e. reachable to other nodes) and reciprocity (i.e. tendency to make reciprocal links) are considered to explore this longitudinal network. ERG models are probabilistic models that are presented by locally determined explanatory variables and can effectively identify structural properties of networks. From the analysis of this email communication network, we notice that students’ network positions and behaviours change with the changes in their study load. In particular, we find that (i) students make an increased number of email communications with the in-crease of study load; (ii) the email communication network become sparse with the increase of study load; and (iii) the 2-star parameter (a subset of three nodes in which one node is connected to each of the other two nodes) and the triangle parameter (a subset of three nodes in which each node is connected to the other two nodes) can effectively explain the formation of network in wave3; whereas, the 3-star parameter (a subset of four nodes in which one node is connected to each of other three nodes) can effectively explain the formation of network in wave1 and wave2. Interpretations of these findings for the monitoring of student behaviour in online learning environments, as well as the implications for the design of assessment and the use of asynchronous tools are discussed in this paper