Trajectories in Epistemic Network Analysis

The understanding of dynamics of data streams is greatly affected by the choice of temporal resolution at which the data are discretized, aggregated, and analyzed. Our paper focuses explicitly on data streams represented as dynamic networks. We propose a framework for identifying meaningful resolution levels that best reveal critical changes in the network structure, by balancing the reduction of noise with the loss of information. We demonstrate the applicability of our approach by analyzing various network statistics of both synthetic and real dynamic networks and using those to detect important events and changes in dynamic network structure.

One of the most characteristic features of biological molecular networks is that the network structure itself changes, depending on the cellular environment. Indeed, activated molecules show a variety of responses to distinctive cell conditions, and subsequently the network structures of active molecules also change. Here we present an approach to trace the network structure changes by using the graphical chain model developed from the gene expression data. The previous procedure for applying the graphical chain model to the expression profiles of a limited number of genes has been improved to analyze the entire set of genes. Furthermore, the chain model has been rearranged according to the association strength, and was scrutinized to identify the candidates of essential gene-gene relationships for the network changes, by using the path consistency algorithm. The improved procedure was applied to the expression profiles of 8,427 genes, which were measured in two distinctive stages of liver cancer progression. As a result, the chain model of the 18 gene cluster relationships with strong associations was inferred, in which the coordination of clusters was described in the cell stage progression, and the gene-gene relationships between known cancer-related genes causing the progression were further refined. Thus, the present procedure is a useful method to model the network structure changes in the cell stage progression, and to clarify the gene candidates for the progression.

Cooperation between ports is known to enhance their competitiveness. This study aims to reveal changes in the global shipping network structure and evaluate the competitiveness of cooperating ports in the shipping network as port connectivity. We apply a backbone flow algorithm, which creates an initial shipping network with backbone flow and reconstructs the network using an adaptive large-neighborhood search, to solve a liner shipping network design problem. Network science measures are used to evaluate changes in network structure and port connectivity. The cooperation between the Osaka and Kobe ports is analyzed as a case study. We find that draft development allows large-sized vessels, which cannot currently call at the Osaka and Kobe ports, to dock there, enhancing their competitiveness. This increased competitiveness surpasses that of the Kaohsiung port but remains lower than that of other large Asian hub ports, including Busan, Singapore, and Tanjung Pelepas. Our findings provide guidelines for developing port strategies to attract more port calls and enhance competitiveness through port cooperation.

The impact of interorganizational systems (IOSs) on the structure of market networks is analyzed from a management perspective. A research framework is applied to various organizational settings, yielding a range of mixed mode forms in which elements of both market and hierarchy are evident. These forms are more complex than the simple network or hybrid structures postulated in the management and information systems literature. The framework represents a departure from electronic markets theory, questioning its basic predictions that as companies trade electronically there will be proportionately more markets than hierarchies. Instead, IOSs make possible relationships that combine market and hierarchy elements simultaneously. Although economic forces are driving the changes in network structure, economic variables are tempered by individual firm strategies reflecting investment, network structure and IOS choices. The implications for theory are that traditional analyses of network structures and competition in business markets do not describe or explain adequately the structure and dynamics of competition in an electronic trading environment. The implications for managers are that they should consider the effects of mixed mode network structures on their processes for forming and managing business relationships supported by IOSs. The contribution of the paper is to provide a more accurate model of competition in business markets by demonstrating that multiple forms of mixed mode network structures exist. The mixed mode proposition is illustrated with case data from a range of mixed mode network structures.

Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social network’s structure and communicative properties. Here, we study how external events are associated with a network’s change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers with different roles in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. We also analyze the communication dynamics among specialized teams in the organization. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness such as the activation of weak ties to obtain novel information. Rather, the network “turtles up.” It displays a propensity for higher clustering, strong tie interaction, and an intensification of insider vs. outsider and within-role vs. between-role communication. Further, we find changes in network structure predict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network.

Agent-based modeling of China’s rural–urban migration and social network structure

Network analysis provides a powerful framework for the interpretation of genome-wide data. While static network approaches have proved fruitful, there is increasing interest in the insights gained from the analysis of cellular networks under different conditions. In this work, we study the effect of stress on cellular networks in fission yeast. Stress elicits a sophisticated and large scale cellular response, involving a shift of resources from cell growth and metabolism towards protection and maintenance. Previous work has suggested that these changes can be appreciated at the network level. In this paper, we study two types of cellular networks: gene co-regulation networks and weighted protein interaction networks. We show that in response to oxidative stress, the co-regulation networks re-organize towards a more modularised structure: while sets of genes become more tightly co-regulated, co-regulation between these modules is decreased. This shift translates into longer average shortest path length, increased transitivity, and decreased modular overlap in these networks. We also find a similar change in structure in the weighted protein interaction network in response to both oxidative stress and nitrogen starvation, confirming and extending previous findings. These changes in network structure could represent an increase in network robustness and/or the emergence of more specialised functional modules. Additionally, we find stress induces tighter co-regulation of non-coding RNAs, decreased functional importance of splicing factors, as well as changes in the centrality of genes involved in chromatin organization, cytoskeleton organization, cell division, and protein turnover.

BackgroundPatients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) show functional and structural connectivity alterations in the default mode network (DMN) while cerebrovascular disease (CeVD) shows functional and structural connectivity changes in the executive control network (ECN). Such disruptions are associated with memory and executive function impairment, respectively. Concurrent AD and CeVD pathology is associated with a higher rate of cognitive decline and differential neurodegenerative patterns. Together, such findings are likely reflective of different underlying pathology in AD with and without CeVD. However, few studies have examined the effect of CeVD on network functional connectivity (task-free functional magnetic resonance imaging (fMRI)) and structural connectivity (diffusion MRI) of the DMN and ECN in aMCI and AD using a hypothesis-driven multiple seed-based approach.MethodsWe examined functional and structural connectivity network changes in 39 aMCI, 50 aMCI+CeVD, 47 AD, 47 AD+CeVD, and 65 healthy controls (HCs) and their associations with cognitive impairment in the executive/attention and memory domains.ResultsWe demonstrate divergent DMN and ECN functional connectivity changes in CeVD and non-CeVD subjects. Compared with controls, intra-DMN hippocampal functional connectivity reductions were observed in both AD and AD+CeVD, while intra-DMN parietal and medial prefrontal-parietal functional connectivity was higher in AD+CeVD and aMCI+CeVD, but lower in AD. Intra-ECN frontal functional connectivity increases and fronto-parietal functional connectivity decreases occurred in CeVD but not non-CeVD subjects. Such functional connectivity alterations were related with cognitive impairment in a dissociative manner: intra-DMN functional connectivity changes were associated with worse cognition primarily in non-CeVD groups, while intra-ECN functional connectivity changes were associated with worse cognition primarily in CeVD groups. Additionally, CeVD and non-CeVD groups showed overlapping and distinct alterations in inter-network DMN-ECN functional connectivity depending on disease severity. In contrast to functional connectivity, CeVD groups had greater network structural connectivity damage compared with non-CeVD groups in both aMCI and AD patients. Network structural connectivity damage was associated with worse cognition.ConclusionsWe demonstrate differential functional and structural network changes between aMCI and AD patients with and without CeVD through diverging and deleterious network-based degeneration underlying domain-specific cognitive impairment.

In nursing education through team simulations, students must learn to position themselves correctly in coordination with colleagues. However, with multiple student teams in action, it is difficult for teachers to give detailed, timely feedback on these spatial behaviours to each team. Indoor-positioning technologies can now capture student spatial behaviours, but relatively little work has focused on giving meaning to student activity traces, transforming low-level x/y coordinates into language that makes sense to teachers. Even less research has investigated if teachers can make sense of that feedback. This paper therefore makes two contributions. (1) Methodologically, we document the use of Epistemic Network Analysis (ENA) as an approach to model and visualise students’ movements. To our knowledge, this is the first application of ENA to analyse human movement. (2) We evaluated teachers’ responses to ENA diagrams through qualitative analysis of video-recorded sessions. Teachers constructed consistent narratives about ENA diagrams’ meaning, and valued the new insights ENA offered. However, ENA’s abstract visualisation of spatial behaviours was not intuitive, and caused some confusions. We propose, therefore, that the power of ENA modelling can be combined with other spatial representations such as a classroom map, by overlaying annotations to create a more intuitive user experience.

Using network ecology to understand and mitigate long‐term insect declines

After the events of 9/11, U.S. counterterrorism became more proactive in that the Patriot Act allowed the authorities far more freedom to directly attack terrorist network structures. We argue that rational terrorists will attempt to thwart such policies and restructure themselves to be less penetrable. We model the trade-off between security and intragroup communication faced by terrorists. The model is used to derive the anticipated changes in network structure and the consequent changes in the type, complexity, and success rate of potential terrorist attacks.

PurposeThis study aims to explore the risk spillover effects among different sectors of the Chinese stock market after the outbreak of COVID-19 from both Internet sentiment and price fluctuations.Design/methodology/approachThe authors develop four indicators used for risk contagion analysis, including Internet investors and news sentiments constructed by the FinBERT model, together with realized and jump volatilities yielded by high-frequency data. The authors also apply the time-varying parameter vector autoregressive (TVP-VAR) model-based and the tail-based connectedness framework to investigate the interdependence of tail risk during catastrophic events.FindingsThe empirical analysis provides meaningful results related to the COVID-19 pandemic, stock market conditions and tail behavior. The results show that after the outbreak of COVID-19, the connectivity between risk spillovers in China's stock market has grown, indicating the increased instability of the connected system and enhanced connectivity in the tail. The changes in network structure during COVID-19 pandemic are not only reflected by the increased spillover connectivity but also by the closer relationships between some industries. The authors also found that major public events could significantly impact total connectedness. In addition, spillovers and network structures vary with market conditions and tend to exhibit a highly connected network structure during extreme market status.Originality/valueThe results confirm the connectivity between sentiments and volatilities spillovers in China's stock market, especially in the tails. The conclusion further expands the practical application and theoretical framework of behavioral finance and also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management across stock sectors.

Gene expression data can be used to identify the activation status of genes and pathways in tumor samples and to infer the structure of regulatory networks. Most tumor datasets such as The Cancer Genome Atlas focus on unperturbed samples, and subsequently, the statistical frameworks that analyze them. We hypothesized that perturbation of cells can uncover network structures that are undetectable in basal state, and reveal the complexity of cancer cells. To demonstrate this, we generated gene expression data from a panel of melanoma cell lines before and after inhibition of MEK or AKT. Here we present a new computational framework to analyze gene expression data before and after perturbations. The algorithm aims to define “differential networks1” - rewiring of network components in different tumors. The algorithm sheds light on the complex and context-specific nature of network structure, including pathways that are activated, or under the control of MAPK or AKT, only in a subset of samples. The perturbation data allowed us to assess pathway activation levels that are undetectable in unperturbed state. For example, AKT is activated in melanoma by PTEN loss, but the effect of NRAS mutation on AKT is unknown, and AKT activation is undetectable in unperturbed cells. Our algorithm identified an AKT activation signature in perturbed state, and helped define the effects of PTEN and NRAS mutations on the pathway. Additionally, the algorithm inferred the genetic status of p53 in each cell line by identifying pathway activation, changes that are, once again, detectable only after perturbations. By linking protein data with the algorithm's results, we identified cell lines with constitutive activation of the STAT3 pathway, and demonstrated the crosstalk between MAPK and STAT3 pathways. Moreover, combining the results from the algorithm with known signatures of drug-sensitivity accurately predicted which drug combinations provided a synergistic effect in each cell line. Associations between pathway activity level and drug sensitivity are known for some drugs. By using our algorithm's ability to infer pathway status in different tumors and to detect changes in those pathways after drug treatment, it correctly predicted when drug treatment sensitized cells to the effects of chemotherapy and other drugs. To conclude, we show that gene expression of perturbed cells greatly enhances the power of network structure analysis. Our results reveal context-specific changes in network structure, and predict which cell lines are sensitized to specific drugs by pathway perturbations. These results have direct applicability to the development of drug combinations in melanoma. 1 Pe'er and Hacohen, Cell 2011 Citation Format: Oren Litvin, Sarit Schwartz, Mark Rocco, Tanya Schild, Neal Rosen, Dana Pe'er. Novel network analysis framework identifies context-specific drug combinations in melanoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5236. doi:10.1158/1538-7445.AM2013-5236 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.

Assessing individual contributions to Collaborative Problem Solving: A network analysis approach

This paper provides a tutorial on epistemic network analysis (ENA), a novel method for identifying and quantifying connections among elements in coded data and representing them in dynamic network models. Such models illustrate the structure of connections and measure the strength of association among elements in a network, and they quantify changes in the composition and strength of connections over time. Importantly, ENA enables comparison of networks both directly and via summary statistics, so the method can be used to explore a wide range of qualitative and quantitative research questions in situations where patterns of association in data are hypothesized to be meaningful. While ENA was originally developed to model cognitive networks—the patterns of association between knowledge, skills, values, habits of mind, and other elements that characterize complex thinking—ENA is a robust method that can be used to model patterns of association in any system characterized by a complex network of dynamic relationships among a relatively small, fixed set of elements.