The network approach to assess the structure of knowledge: Storage, distribution and retrieval as three measures in analysing concept maps
We present three new standardised network concept map (CM) measures that can provide unique information about learning‐related progress, which cannot be determined from previously known measures. Grounded in cognitive development theory on the one hand, and network theory on the other hand, our measures reveal how knowledge is stored, distributed and retrieved. We validated the new measures by testing their ability to discriminate between CMs of respondents with different levels of competency in statistics (students before and after taking an introductory statistics course and experts in the field of statistics). We also validated our measures against the most commonly used traditional and network measures. Based on a small sample of respondents, we show that two of the newly proposed compound measures reveal significant differences between experts and novices in the field, with higher values for experts, showing that expert knowledge is better distributed, more connected and balanced. More importantly, our measures were sensitive enough to show learning‐related progress for students, albeit statistically non‐significant, while common indicators from network theory did not demonstrate these small shifts. The validity of our new measures can be inferred from the consistency of the results from different sets of measures.
Methods of network analysis are used in this paper for mapping the local academic community of St. Petersburg sociologists. The survey data on relations between individual scholars serve as a guide in reconstruction of the communitys network history as well as a system of independent variables in accounting for differences between its various natural zones. In this manner, the paper explores the points of convergence between Chicago school social ecology and modern social network analysis.
This volume contains two types of papers—a selection of contributions from the “Second International Conference in Network Analysis” held in Nizhny Novgorod on May 7–9, 2012, and papers submitted to an "open call for papers" reflecting the activities of LATNA at the Higher School for Economics.
This volume contains many new results in modeling and powerful algorithmic solutions applied to problems in
- vehicle routing
- single machine scheduling
- modern financial markets
- cell formation in group technology
- brain activities of left- and right-handers
- speeding up algorithms for the maximum clique problem
- analysis and applications of different measures in clustering
The broad range of applications that can be described and analyzed by means of a network brings together researchers, practitioners, and other scientific communities from numerous fields such as Operations Research, Computer Science, Bioinformatics, Medicine, Transportation, Energy, Social Sciences, and more. The contributions not only come from different fields, but also cover a broad range of topics relevant to the theory and practice of network analysis. Researchers, students, and engineers from various disciplines will benefit from the state-of-the-art in models, algorithms, technologies, and techniques including new research directions and open questions.
The article introduces a historical-sociological research project reconstructing intellectual and institutional transformations of post-soviet social sciences in the last 25 years. The projects ambition was to achieve this aim via applying classical community study research strategy and various methods derived from social science history to the case of St. Petersburg sociologists. We identified 622 individuals as St. Petersburg sociologists and traced records of their institutional trajectories, appearance in print, citing behaviour, social networks, political attitudes, sources of income, professional authorities, and attention spaces through 25 years.
This volume contains a selection of contributions from the "First International Conference in Network Analysis," held at the University of Florida, Gainesville, on December 14-16, 2011. The remarkable diversity of fields that take advantage of Network Analysis makes the endeavor of gathering up-to-date material in a single compilation a useful, yet very difficult, task. The purpose of this volume is to overcome this difficulty by collecting the major results found by the participants and combining them in one easily accessible compilation.
We apply Dempster-Shafer theory in order to reveal important elements in undirected weighted networks. We estimate cooperation of each node with different groups of vertices that surround it via construction of belief functions. The obtained intensities of cooperation are further redistributed over all elements of a particular group of nodes that results in pignistic probabilities of node-to-node interactions. Finally, pairwise interactions can be aggregated into the centrality vector that ranks nodes with respect to derived values. We also adapt the proposed model to multiplex networks. In this type of networks nodes can be differently connected with each other on several levels of interaction. Various combination rules help to analyze such systems as a single entity, that has many advantages in the study of complex systems. In particular, Dempster rule takes into account the inconsistency in initial data that has an impact on the final centrality ranking. We also provide a numerical example that illustrates the distinctive features of the proposed model. Additionally, we establish analytical relations between a proposed measure and classical centrality measures for particular graph configurations.
This article presents the up-to-date views on the continuous education and the tendencies to business-education development. The learning during the whole life is the vital necessity of our days. The peculiarity of the continuous education consists in the way of the payment for it. It can be payed by the organization which is interested in the improvement of the professional skills of the employees or by the employees themselves, if they participate in the continuous education program. In the framework of this logic the business -education is some special field of the continuous education. Now days business-education and continuous education are some definite system.
Semantic network reduction is considered in application to visual analytics of relational data. Merging structurally equivalent nodes it is straightforward to construct a reduced semantic network that completely species the initial structure of relations between nodes. This paper presents the analysis of such reduction applied to the communication network from Stanford Large Network Dataset Collection. It is shown how the reduction based on structural equivalence can help in visualization of large semantic networks.
The distractive effects on attentional task performance in different paradigms are analyzed in this paper. I demonstrate how distractors may negatively affect (interference effect), positively (redundancy effect) or neutrally (null effect). Distractor effects described in literature are classified in accordance with their hypothetical source. The general rule of the theory is also introduced. It contains the formal prediction of the particular distractor effect, based on entropy and redundancy measures from the mathematical theory of communication (Shannon, 1948). Single- vs dual-process frameworks are considered for hypothetical mechanisms which underpin the distractor effects. Distractor profiles (DPs) are also introduced for the formalization and simple visualization of experimental data concerning the distractor effects. Typical shapes of DPs and their interpretations are discussed with examples from three frequently cited experiments. Finally, the paper introduces hierarchical hypothesis that states the level-fashion modulating interrelations between distractor effects of different classes.