Network theory

About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.

Entropy-based approach to missing-links prediction

Abstract: Link-prediction is an active research field within network theory, aiming at uncovering missing connections or predicting the emergence of future relationships from the observed network structure. This paper represents our contribution to the stream of research concerning missing links prediction. Here, we propose an entropy-based method to predict a given percentage of missing links, by identifying them with the most probable non-observed ones. The probability coefficients are computed by solving opportunely defined null-models over the accessible network structure. Upon comparing our likelihood-based, local method with the most popular algorithms over a set of economic, financial and food networks, we find ours to perform best, as pointed out by a number of statistical indicators (e.g. the precision, the area under the ROC curve, etc.). Moreover, the entropy-based formalism adopted in the present paper allows us to straightforwardly extend the link-prediction exercise to directed networks as well, thus overcoming one of the main limitations of current algorithms. The higher accuracy achievable by employing these methods - together with their larger flexibility - makes them strong competitors of available link-prediction algorithms.

Degree centrality based on the weighted network

Abstract: Node centrality has been widely studied in the complex networks. In 2010, the model of node centrality under the weighted network was obtained by Tore Opashl et al. Tie weights and the number of ties were connected with certain proportion by tuning parameter in the model. However, the proportion is random measure. In this paper, the selection standard of the optimal turning parameters is proposed. In the proposed method, the maximum degree centrality of node can be emphasized. The numerical example of weighted network on optimal value selection is used to show the efficiency of the method

Dynamical networks: Finding, measuring, and tracking neural population activity using network science.

Abstract: Systems neuroscience is in a headlong rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons come the inescapable problems of visualizing, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can visualize and describe an arbitrarily large recording. I show that with this description we can quantify the effects of manipulating a neural circuit, track changes in population dynamics over time, and quantitatively define theoretical concepts of neural populations such as cell assemblies. Using network science as a core part of analyzing population recordings will thus provide both qualitative and quantitative adva...

A Methodology for Integrating Network Theory and Topic Modeling and its Application to Innovation Diffusion

Abstract: Text data pertaining to socio-technical networks often are analyzed separately from relational data, or are reduced to the fact and strength of the flow of information between nodes. Disregarding the content of text data for network analysis can limit our understanding of the effects of language use in networks. We present a computational and interdisciplinary methodology that addresses this limitation by combining theory from socio-linguistics with social network analysis and machine learning based text mining: we use network analysis to identify groups of individuals who assume the theoretically grounded roles of change agents and preservation agents. People in these roles differ in their motivation and capability to induce and adopt change in a network. Topic modeling is then constrained to the texts authored by people in these roles. We apply this methodology to a public dataset of about 55,000 research proposals that were granted funding. Our results suggest that the people per role differ in the research domains they work on and the strength of association with those domains that both roles are involved with, but are similar with respect to fulfilling the task or additional role of being a project manager.