The evolutions of the rich get richer and the fit get richer phenomena in scholarly networks: the case of the strategic management journal

Understanding how a scientist develops new scientific collaborations or how their papers receive new citations is a major challenge in scientometrics. The approach being proposed simultaneously examines the growth processes of the co-authorship and citation networks by analyzing the evolutions of the rich get richer and the fit get richer phenomena. In particular, the preferential attachment function and author fitnesses, which govern the two phenomena, are estimated non-parametrically in each network. The approach is applied to the co-authorship and citation networks of the flagship journal of the strategic management scientific community, namely the Strategic Management Journal. The results suggest that the abovementioned phenomena have been consistently governing both temporal networks. The average of the attachment exponents in the co-authorship network is 0.30 while it is 0.29 in the citation network. This suggests that the rich get richer phenomenon has been weak in both networks. The right tails of the distributions of author fitness in both networks are heavy, which imply that the intrinsic scientific quality of each author has been playing a crucial role in getting new citations and new co-authorships. Since the total competitiveness in each temporal network is founded to be rising with time, it is getting harder to receive a new citation or to develop a new collaboration. Analyzing the average competency, it was found that on average, while the veterans tend to be more competent at developing new collaborations, the newcomers are likely better at acquiring new citations. Furthermore, the author fitness in both networks has been consistent with the history of the strategic management scientific community. This suggests that coupling node fitnesses throughout different networks might be a promising new direction in analyzing simultaneously multiple networks.     

Topic scientific community in science: a combined perspective of scientific collaboration and topics

Scientific communities are clusters of researchers and play important roles in modern science. Studying different forms of scientific communities that either physically or virtually exist is a feasible way to disclose underlying mechanisms of science. From the perspective of complex networks, topology-based communities and topic-based communities reflect scientific collaboration and topical features of science respectively. However, the two features are not isolated but intertwined in scientific practice. This study proposes an approach to detect Topical Scientific Communities (TSCs) with both topology and topic features by applying machine learning techniques and network theory. As an example, the TSCs of the informetrics field are detected, and then the characteristics of these TSCs are analyzed. It is shown that collaboration patterns on the topic level can be revealed by analyzing the static network structure and dynamics of TSCs. Furthermore, cross-topic collaborations at multiple levels could be investigated through TSCs. In addition, TSCs can effectively organize researchers in terms of productivity. Future work will further explore and generalize characteristics of TSCs, and the applications of TSCs to other tasks of studying science.     

The Matthew effect in empirical data

The Matthew effect describes the phenomenon that in societies, the rich tend to get richer and the potent even more powerful. It is closely related to the concept of preferential attachment in network science, where the more connected nodes are destined to acquire many more links in the future than the auxiliary nodes. Cumulative advantage and success-breads-success also both describe the fact that advantage tends to beget further advantage. The concept is behind the many power laws and scaling behaviour in empirical data, and it is at the heart of self-organization across social and natural sciences. Here, we review the methodology for measuring preferential attachment in empirical data, as well as the observations of the Matthew effect in patterns of scientific collaboration, socio-technical and biological networks, the propagation of citations, the emergence of scientific progress and impact, career longevity, the evolution of common English words and phrases, as well as in education and brain development. We also discuss whether the Matthew effect is due to chance or optimization, for example related to homophily in social systems or efficacy in technological systems, and we outline possible directions for future research.     

Linking networks and plant roles: the impact of changing a plant role

Many manufacturing firms are expanding their global footprint to explore new opportunities for efficient and effective production. The strategic perspective on international manufacturing networks involves both the network level and the plant level. A key aspect is the relationship between the network and the role of plants. In this research, we investigate the relationship between the network and plant perspectives in international manufacturing networks. We use an embedded case study that includes five plants in two product networks over a period of three years. We analyse how changing the role of one plant affects the network as well as the roles of the other plants in the networks. We find that decisions on plant roles are, to a very high degree, network decisions and not decisions for individual plants. Based on the insights into the case study, we also develop a framework for mapping manufacturing networks, including market coverage, plant location and site competence.     

Multiplexity,Growth Mechanisms and Structural Variety in Scientific Collaboration Networks

A substantial body of literature has recently been concerned with the structure and dynamics of the collaboration networks that underlie the production and dissemination of scientific knowledge. Despite the growing interest in these networks, relatively little emphasis has been placed on two broad areas of investigation: on the one hand, the interplay of the growth mechanisms underpinning the evolution of collaborative arrangements; on the other, the implications that the structure and multiplexity of these arrangements have on knowledge creation and diffusion. The articles in this Special Issue aim to bridge these gaps in the literature and, by embracing a variety of perspectives, contribute towards a better understanding of how collaboration networks originate, function, and evolve. This Introduction offers a theoretical and methodological framework for the articles here included. It begins by discussing problems of measurement of scientific collaboration, and goes on to examine the role that growth mechanisms, structural variety and multiplexity play in shaping the genesis and functioning of collaboration networks. In reviewing current and emerging research themes, the discussion will also identify promising research directions that will stimulate future work on collaboration networks in science and technology.     

Blockmodeling of co-authorship networks in library and information science in Argentina: a case study

The paper introduces the use of blockmodeling in the micro-level study of the internal structure of co-authorship networks over time. Variations in scientific productivity and researcher or research group visibility were determined by observing authors?? role in the core-periphery structure and crossing this information with bibliometric data. Three techniques were applied to represent the structure of collaborative science: (1) the blockmodeling; (2) the Kamada-Kawai algorithm based on the similarities in co-authorships present in the documents analysed; (3) bibliometrics to determine output volume, impact and degree of collaboration from the bibliographic data drawn from publications. The goal was to determine the extent to which the use of these two complementary approaches, in conjunction with bibliometric data, provides greater insight into the structure and characteristics of a given field of scientific endeavour. The paper describes certain features of Pajek software and how it can be used to study research group composition, structure and dynamics. The approach combines bibliometric and social network analysis to explore scientific collaboration networks and monitor individual and group careers from new perspectives. Its application on a small-scale case study is intended as an example and can be used in other disciplines. It may be very useful for the appraisal of scientific developments.     

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.     

Internet practice and professional networks in Chilean science: Dependency or progress?

The conventional view depicts scientific communities in the developing world as globally isolated and dependent. Recent studies suggest that individual scientists tend to favor either local or international ties. Yet there are good reasons to believe that both kinds of ties are beneficial for knowledge production. Since they allow for the more efficient management of social networks, Internet technologies are expected to resolve this inverse relationship. They are also expected to decentralize access to resources within developing regions that have traditionally reflected an urban male bias. Elaborating upon science, development and social network perspectives, we examine the impact of the Internet in the Chilean scientific community, addressing the questions ‘to what extent is Internet use and experience associated with the size of foreign and domestic professional networks?’ and ‘are professional network resources equitably distributed across regional and demographical dimensions?’ We offer results from a communication network survey of 337 Chilean researchers working in both academic departments and research institutes. We introduce a new measure, ‘collaboration range’, to indicate the extent to which scientists engage in work with geographically dispersed contacts. Results suggest that larger foreign networks are associated with higher email use and diversity, but local networks are smaller with longer use of the Internet. Diversity of email use is also associated with diverse geographical networks. Moreover, Internet use may be reducing the significance of international meetings for scientific collaboration and networking. Finally, results also show that in the Internet age professional network resources are distributed symmetrically throughout the Chilean scientific community.     

Gene divergence and pathway duplication in the metabolic network of yeast and digital organisms

We have studied the metabolic gene–function network in yeast and digital organisms evolved in the artificial life platform Avida. The gene–function network is a bipartite network in which a link exists between a gene and a function (pathway) if that function depends on that gene, and can also be viewed as a decomposition of the more traditional functional gene networks, where two genes are linked if they share any function. We show that the gene–function network exhibits two distinct degree distributions: the gene degree distribution is scale-free while the pathway distribution is exponential. This is true for both yeast and digital organisms, which suggests that this is a general property of evolving systems, and we propose that the scale-free gene degree distribution is due to pathway duplication, i.e. the development of a new pathway where the original function is still retained. Pathway duplication would serve as preferential attachment for the genes, and the experiments with Avida revealed precisely this; genes involved in many pathways are more likely to increase their connectivity. Measuring the overlap between different pathways, in terms of the genes that constitute them, showed that pathway duplication also is a likely mechanism in yeast evolution. This analysis sheds new light on the evolution of genes and functionality, and suggests that function duplication could be an important mechanism in evolution.     

Demand network alignment: aligning the physical,informational and relationship issues in supply chains

The aim of this paper is to propose a model for the inter-relationship and alignment of supply and demand networks from two perspectives namely the physical/operational (including information/knowledge systems), and the relationship/behavioural that affect collaborating partners. The two perspectives need to be considered simultaneously to realize the dynamics of supply chains and the issues affecting their management. The paper presents the development of the model on a conceptual level, and utilizes the data from a case study to depict the effect of these two perspectives in understanding supply chain dynamics.     

An evolutionary process of global nanotechnology collaboration: a social network analysis of patents at USPTO

Using social network analysis to examine patenting data available at the USPTO, this paper explores an evolutionary process of global nanotechnology collaboration network from the perspective of entry and exit of collaborative organizations (nodes) and network’s preferential attachment process. The results show that the nanotechnology collaboration network evolved through frequent updates of the nodes and their relations (links). Compared with degree centrality and closeness centrality, betweenness centrality of an existing node was a significantly better predictor of the preferential attachment. The nodes with higher betweenness centrality were more influential to attract other nodes. This fact is observed while the network evolved. The results reveal that the core nodes with higher betweenness centrality were mostly large organizations that were equipped with core technology. They played an important broker role attracting more organizations into collaboration.     

Functionalized Conducting Polymer Nano‐Networks from Controlled Oxidation Polymerization toward Cell Engineering

In this work we present a general approach for bulk synthesis of various functionalized conducting polymer nano‐networks. 3,4‐ethylenedioxythiophene (EDOT) dimers are used to initiate the chemical polymerization of functionalized EDOT in the solvent system with high polarity, which leads to better control of the oxidation polymerization. Under these reaction conditions, various functionalized EDOT monomers form polymeric nano‐network structures. We also evaluate the cell growth and cell viability on these conducting polymer nano‐networks. The nano‐networks provide highly biocompatible materials for PC12 cells and they show nice attachment on the surface. These properties of functionalized conducting polymer nano‐networks indicate a promising platform for cell engineering.     

Modeling idiosyncratic properties of collaboration networks revisited

A study on the network characteristics of two collaboration networks constructed from the ACM and DBLP digital libraries is presented. Different types of generic network models and several examples are reviewed and experimented on re-generating the collaboration networks. The results reveal that while these models can generate the power-law degree distribution sufficiently well, they are not able to capture the other two important dynamic metrics: average distance and clustering coefficient. While all current models result in small average distances, none shows the same tendency as the real networks do. Furthermore all models seem blind to generating large clustering coefficients. To remedy these shortcomings, we propose a new model with promising results. We get closer values for the dynamic measures while having the degree distribution still power-law by having link addition probabilities change over time, and link attachment happen within local neighborhood only or globally, as seen in the two collaboration networks.     

A semantic e-Kanban system for network-centric manufacturing: technology,scale-free convergence,value and cost-sharing considerations

It is critical to facilitate business partners to be seamlessly interoperable with each other as the network-centric manufacturing (NCM) paradigm becomes a major trend in today's manufacturing environment. In this work, we propose a semantic e-Kanban inventory system where a semantic gateway is acting as a mapping hub to enable heterogeneous messages to be seamlessly exchanged between business partners on demand. The semantic gateway uses reasoning rules to map business partners’ proprietary data schemas and provides interoperability required for NCM. To observe the network dynamics of our proposed system, a discrete time dynamic model is built and shows its growth to a scale-free network with a convergence rate depending on the initial connectivity of the semantic gateway and preferential attachment parameters. To analyse the proposed system from an economic perspective, analytical and numerical studies are conducted showing that it has enough potential to reduce supply chain costs in comparison to those of the traditional approach. Finally, this study attempts to address the cost-sharing issue encountered when multiple partners are direct beneficiaries of the efficiency gain through a joint network-centric capability, but it is questionable who should pay for the capability implementation. The cost-sharing issue must be investigated because most network-centric capabilities would not be economically or technically feasible for an individual company to achieve.     

The cost of attack in competing networks

Real-world attacks can be interpreted as the result of competitive interactions between networks, ranging from predator–prey networks to networks of countries under economic sanctions. Although the purpose of an attack is to damage a target network, it also curtails the ability of the attacker, which must choose the duration and magnitude of an attack to avoid negative impacts on its own functioning. Nevertheless, despite the large number of studies on interconnected networks, the consequences of initiating an attack have never been studied. Here, we address this issue by introducing a model of network competition where a resilient network is willing to partially weaken its own resilience in order to more severely damage a less resilient competitor. The attacking network can take over the competitor''s nodes after their long inactivity. However, owing to a feedback mechanism the takeovers weaken the resilience of the attacking network. We define a conservation law that relates the feedback mechanism to the resilience dynamics for two competing networks. Within this formalism, we determine the cost and optimal duration of an attack, allowing a network to evaluate the risk of initiating hostilities.     

Italian sociologists: a community of disconnected groups

Examining coauthorship networks is key to study scientific collaboration patterns and structural characteristics of scientific communities. Here, we studied coauthorship networks of sociologists in Italy, using temporal and multi-level quantitative analysis. By looking at publications indexed in Scopus, we detected research communities among Italian sociologists. We found that Italian sociologists are fractured in many disconnected groups. The giant connected component could be split into five main groups with a mix of three main disciplinary topics: sociology of culture and communication (present in two groups), economic sociology (present in three groups) and general sociology (present in three groups). By applying an exponential random graph model, we found that collaboration ties are mainly driven by the research interests of these groups. Other factors, such as preferential attachment, gender and affiliation homophily are also important, but the effect of gender fades away once other factors are controlled for. Our research shows the advantages of multi-level and temporal network analysis in revealing the complexity of scientific collaboration patterns.

     

Evolutionary paths of change of emerging nanotechnological innovation systems: the case of ZnO nanostructures

This paper puts forward a quantitative approach aimed at the understanding of the evolutionary paths of change of emerging nanotechnological innovation systems. The empirical case of the newly emerging zinc oxide one-dimensional nanostructures is used. In line with other authors, ‘problems’ are visualized as those aspects guiding the dynamics of innovation systems. It is argued that the types of problems confronted by an innovation system, and in turn its dynamics of change, are imprinted on the nature of the underlying knowledge bases. The latter is operationalized through the construction of co-citation networks from scientific publications. We endow these co-citation networks with directionality through the allocation of a particular problem, drawn from a ‘problem space’ for nanomaterials, to each network node. By analyzing the longitudinal, structural and cognitive changes undergone by these problem-attached networks, we attempt to infer the nature of the paths of change of emerging nanotechnological innovation systems. Overall, our results stress the evolutionary mechanisms underlying change in a specific N&N subfield. It is observed that the latter may exert significant influence on the innovative potentials of nanomaterials.     

Unsupervised learning of control signals and their encodings in Caenorhabditis elegans whole-brain recordings

A major goal of computational neuroscience is to understand the relationship between synapse-level structure and network-level functionality. Caenorhabditis elegans is a model organism to probe this relationship due to the historic availability of the synaptic structure (connectome) and recent advances in whole brain calcium imaging techniques. Recent work has applied the concept of network controllability to neuronal networks, discovering some neurons that are able to drive the network to a certain state. However, previous work uses a linear model of the network dynamics, and it is unclear if the real neuronal network conforms to this assumption. Here, we propose a method to build a global, low-dimensional model of the dynamics, whereby an underlying global linear dynamical system is actuated by temporally sparse control signals. A key novelty of this method is discovering candidate control signals that the network uses to control itself. We analyse these control signals in two ways, showing they are interpretable and biologically plausible. First, these control signals are associated with transitions between behaviours, which were previously annotated via expert-generated features. Second, these signals can be predicted both from neurons previously implicated in behavioural transitions but also additional neurons previously unassociated with these behaviours. The proposed mathematical framework is generic and can be generalized to other neurosensory systems, potentially revealing transitions and their encodings in a completely unsupervised way.     

Tracing knowledge diffusion

Knowledge diffusion is the adaptation of knowledge in a broad range of scientific and engineering research and development. Tracing knowledge diffusion between science and technology is a challenging issue due to the complexity of identifying emerging patterns in a diverse range of possible processes. In this article, we describe an approach that combines complex network theory, network visualization, and patent citation analysis in order to improve the means for the study of knowledge diffusion. In particular, we analyze patent citations in the field of tissue engineering. We emphasize that this is the beginning of a longer-term endeavor that aims to develop and deploy effective, progressive, and explanatory visualization techniques for us to capture the dynamics of the evolution of patent citation networks. The work has practical implications on resource allocation, strategic planning, and science policy. This revised version was published online in June 2006 with corrections to the Cover Date.     

Adaptive coevolutionary networks: a review.

Adaptive networks appear in many biological applications. They combine topological evolution of the network with dynamics in the network nodes. Recently, the dynamics of adaptive networks has been investigated in a number of parallel studies from different fields, ranging from genomics to game theory. Here we review these recent developments and show that they can be viewed from a unique angle. We demonstrate that all these studies are characterized by common themes, most prominently: complex dynamics and robust topological self-organization based on simple local rules.