Network theory

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

Problemistic Search of the Embedded Firm: The Joint Effects of Performance Feedback and Network Positions on Venture Capital Firms’ Risk Taking

Abstract: The Behavioral Theory of the Firm suggests that performance below aspirations triggers problemistic search that can lead to risk taking. This prediction has received empirical support from most studies on the topic. However, this literature has typically focused on the internal determinants of firm search and risk-taking behavior and given little attention to the influences of social networks in which firms are embedded. To this end, we incorporate the network embeddedness perspective regarding firms’ network positions and their roles in firm decision making. We suggest that a firm’s search behavior is jointly directed by its performance feedback and network positions. Specifically, network brokerage and centrality play important yet distinct roles in guiding firm search behavior by differentially shaping the direction of problemistic search: high brokerage directs problemistic search to high-risk solutions, whereas high centrality directs problemistic search to low-risk solutions. Our theoretical predictions receive general empirical support based on analyses using longitudinal data from the Chinese venture capital industry. Our approach incorporates the crucial role of network structures into the problemistic search model and works toward building a problemistic search theory of the embedded firm.

Analyzing the evolutionary mechanisms of the air transportation system-of-systems using network theory and machine learning algorithms

Abstract: Complexity in the Air Transportation System (ATS) arises from the intermingling of many independent physical resources, operational paradigms, and stakeholder interests, as well as the dynamic variation of these interactions over time. Currently, trade-offs and cost benefit analyses of new ATS concepts are carried out on system-wide evaluation simulations driven by air traffic forecasts that assume fixed airline routes. However, this does not well reflect reality as airlines regularly add and remove routes. A airline service route network evolution model that projects route addition and removal was created and combined with state-of-the-art air traffic forecast methods to better reflect the dynamic properties of the ATS in system-wide simulations. Guided by a system-of-systems framework, network theory metrics and machine learning algorithms were applied to develop the route network evolution models based on patterns extracted from historical data. Constructing the route addition section of the model posed the greatest challenge due to the large pool of new link candidates compared to the actual number of routes historically added to the network. Of the models explored, algorithms based on logistic regression, random forests, and support vector machines showed best route addition and removal forecast accuracies at approximately 20% and 40%, respectively, when validated with historical data. The combination of network evolution models and a system-wide evaluation tool quantified the impact of airline route network evolution on air traffic delay. The expected delay minutes when considering network evolution increased approximately 5% for a forecasted schedule on 3/19/2020. Performance trade-off studies between several airline route network topologies from the perspectives of passenger travel efficiency, fuel burn, and robustness were also conducted to provide bounds that could serve as targets for ATS transformation efforts. The series of analysis revealed that high robustness is achievable only in exchange of lower passenger travel and fuel burn efficiency. However, increase in the network density can mitigate this trade-off.

Centrality-Based Connected Dominating Sets for Complex Network Graphs

Abstract: The author proposes the use of centrality-metrics to determine connected dominating sets CDS for complex network graphs. The author hypothesizes that nodes that are highly ranked by any of these four well-known centrality metrics such as the degree centrality, eigenvector centrality, betweeness centrality and closeness centrality are likely to be located in the core of the network and could be good candidates to be part of the CDS of the network. Moreover, the author aims for a minimum-sized CDS fewer number of nodes forming the CDS and the core edges connecting the CDS nodes while using these centrality metrics. The author discusses our approach/algorithm to determine each of these four centrality metrics and run them on six real-world network graphs ranging from 34 to 332 nodes representing various domains. The author observes the betweeness centrality-based CDS to be of the smallest size in five of the six networks and the closeness centrality-based CDS to be of the smallest size in the smallest of the six networks and incur the largest size for the remaining networks.

Understanding complexity via network theory: a gentle introduction

Abstract: Network theory provides tools which are particularly appropriate for assessing the complex interdependencies that characterise our modern connected world. This article presents an introduction to network theory, in a way that doesn't require a strong mathematical background. We explore how network theory unveils commonalities in the interdependency profiles of various systems, ranging from biological, to social, and artistic domains. Our aim is to enable an intuitive understanding while conveying the fundamental principles and aims of complexity science. Additionally, various network-theoretic tools are discussed, and numerous references for more advanced materials are provided.

Product rating prediction using trust relationships in social networks

Abstract: Traditional recommender systems assume that all users are independent and identically distributed, and ignores the social interactions and connections between users. These issues hinder the recommender systems from providing more personalized recommendations to the users. In this paper, we propose a social trust model and use the probabilistic matrix factorization method to estimate users taste by incorporating user-item rating matrix. The effect of users friends tastes is modeled using a trust model which is defined based on importance (i.e., centrality) and similarity between users. Similarity is modeled using Vector Space Similarity (VSS) algorithm and centrality is quantified using two different centrality measures (degree and eigen-vector centrality). To validate the proposed method, rating estimation is performed on the Epinions dataset. Experiments show that our method provides better prediction when using trust relationship based on centrality and similarity values rather than using the binary values. The contributions of centrality and similarity in the trust values vary with different measures of centrality.