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The purpose of this paper is to study the moving consensus of multi‐agent dynamical systems with time delays and directed weighted networks.

The approach used in the study, the topologies of multi‐agent dynamical systems with directed weighted networks is graph theories. The frequency domain is applied to research the movement characteristics of multi‐agent systems with time delays. The generalized Nyquist criterion and curvature theorem are utilized to analyze the consensus algorithm with heterogeneous input delays and heterogeneous communication delays.

It was discovered that the consensus for the delayed multi‐agent systems with asymmetric coupling weights can be achieved with the hypothesis of directed weighted network composed of n agents with a globally reachable node. The convergence condition is a decentralized consensus condition which uses only local information of each agent.

The novelty associated with this work is to present a new approach to study the consensus of delayed multi‐agent dynamical systems with directed weighted networks. The consensus condition obtained in the paper is less conservative than the consensus condition given in references.

This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

The purpose of this paper is to measure the systemic importance of industry in the world economic system under the system-wide event – the crisis of 2008-2009, by viewing this system as a weighted directed network of interconnected industries.

First, the authors investigate this crisis at three different levels based on network-related indicators: the “macro” global level, the “meso” country level, and the “micro” industry level. This investigation not only provides evidence for the systemic influence, that is, systemic risk, of the crisis, but also reveals the contagion mechanism of the crisis, which supports the stress testing. Second, the authors use a network-related business intelligence algorithm, the combined hyperlink-induced topic search (HITS) algorithm, to measure the contribution of a given individual industry to the overall risk of the economic system or, in other words, the systemic importance of the individual industry.

The HITS algorithm considers both the market information and the interconnectedness of the industries. Based on the stress testing, the performance of the combined HITS is compared with the purely market-based systemic risk measurement. The results show that the combined HITS outperforms the baseline in finding the top N systemically important industries.

The combined HITS algorithm provides a novel network-based perspective of systemic risk measurement.

Measuring the systemic importance based on the combined HITS algorithm can help managers and regulators design effective risk management policies. In this respect, the work initiates a research direction of studying the systemic risk in a business system based on a network-related business intelligence algorithm because the business system can be viewed as an interconnected network.

The purpose of this paper is to apply an analysis of complex networks to empirically research international agricultural commodity trade and countries’ trading relations. The structure of global agricultural commodity trade is quantitatively described and analysed.

Based on statistical physics and graph theory, the research paradigm of a complex network, which has sprung up in the last decade, provides us with new global perspective to discuss the topic of international trade, especially agricultural commodity trade. In this paper, the authors engage in the issue of countries’ positions in international agricultural commodity trade using the latest complex network theories. The authors at first time introduce the improved bootstrap percolation to simulate cascading influences following the breaking down of bilateral agricultural commodity trade relations.

On a mid-level structure, countries are classified into three communities that reflect the structure of the “core/periphery” using the weighted extremal optimisation algorithm and the coarse graining process. On a micro-level, countries’ rankings are provided with the aid of network’s node centralities, which presents world agricultural commodity trade as a closed, imbalanced, diversified and multi-polar development.

The authors at first time introduce the improved bootstrap percolation to simulate cascading influences following the breaking down of bilateral agricultural commodity trade relations.

The purpose of this paper is to present a new methodology to assess the outward connectivity among regional economies in the European Union (EU) and derives policy lessons for the design of regional innovation and competitiveness-enhancing strategic frameworks, with particular reference to research and innovation strategies for smart specialisation (RIS3).

The authors study the network of inter-regional trade flows in the EU25 in the year 2007. Trade data are taken from the PBL Netherlands Environmental Assessment Agency database and mapped onto weighted directed networks in which the nodes represent regions and the links are flows of goods. The authors measure several structural characteristics of the networks, both global properties and centrality indicators describing the position of individual regions within the system.

European regions appear to be mostly integrated in the European single market. Strengths and weaknesses of individual regions are discussed based on rankings obtained from network centrality indicators. Specific policy implications in the context of RIS3 are derived in the case of the Spanish region of Andalusia.

The authors show the potential of the methodology for providing a new family of indicators of the external connectivity of regional economies that can be used by regions wishing to develop their own RIS3 for 2014-2020, as required by the EU in the context of the new cohesion policy framework.

The characteristics of a EU-wide inter-regional network of trade flows are obtained and thoroughly discussed for the first time. A unique and original instrument suitable for inter-regional comparison is developed and tested.

As social media applications have turned into popular platforms for interacting with customers, creation of a consistent customer experience in social commerce has attracted the attention of many practitioners and academics. The migration to create and manage customer experience in social commerce has become an essential issue that will bring new challenges for companies. Despite the increasing investment in this area, few studies have been conducted on the challenges of managing customer experience in social commerce. To fill this theoretical gap, the current study aims at comprehensively exploring the main challenges of customer experience management (CEM) in social commerce and investigating their importance and possible effects in relation to each other.

Using the mixed method, first, the main challenges regarding CEM in social commerce were identified by reviewing the related literature. Then the challenges were enriched and categorized by expert opinions. Next, the challenges and the categorizations were confirmed by conducting a survey analysis applying the t-test and the factor analysis method. Afterwards, the main challenges were identified and weighted. Finally, the Social Network Analysis (SNA) approach was applied to investigate the causal relationship network among the challenges.

The results indicated that the main challenges of CEM in social commerce can be categorized into eight groups. Their weights and causal effects were calculated to identify the high priority challenges. By calculating the main SNA metrics such as degree and betweenness centralities, the high priority challenges of CEM in social commerce were identified. It was revealed that challenges with high out-degree centrality can create many other challenges and those with high betweenness centrality act as intermediary points, through which cause challenges may create effect challenges.

The research results can help marketers to get a big picture of the challenges to successfully implement CEM in social commerce and select the appropriate migration strategies more effectively. They are further recommended to pay due attention to customers' issues as well as the organizational challenges of CEM in social commerce.

Social media has become a priority for businesses to create and improve the customer experience; yet there is no tool to identify the challenges of CEM in this context. This study addresses the overlooked but critically important area of social commerce. The most important contribution of this research is an attempt to provide a comprehensive and integrated framework of the challenges in implementing CEM in social commerce and explore the causal effects they may have on creation of other challenges using SNA.

This study reviews the methods found in the literature for the ranking of authors, identifies the pros and cons of these methods, discusses and compares these methods. The purpose of this paper is to study is to find the challenges and future directions of ranking of academic objects, especially authors, for future researchers.

This study reviews the methods found in the literature for the ranking of authors, classifies them into subcategories by studying and analyzing their way of achieving the objectives, discusses and compares them. The data sets used in the literature and the evaluation measures applicable in the domain are also presented.

The survey identifies the challenges involved in the field of ranking of authors and future directions.

To the best of the knowledge, this is the first survey that studies the author ranking problem in detail and classifies them according to their key functionalities, features and way of achieving the objective according to the requirement of the problem.

The purpose of this paper is to propose a data-driven model to predict credit risks of actors collaborating within a supply chain finance (SCF) network based on the analysis of their network attributes. This can support applying reverse factoring mechanisms in SCFs.

Based on network science, the network measures of the actors collaborating in the investigated SCF are derived through a social network analysis. Then several supervised machine learning algorithms are applied to predict the credit risks of the actors on the basis of their network level and organizational-level characteristics. For this purpose, a data set from an SCF within an automotive industry in Iran is used.

The findings of the research clearly demonstrate that considering the network attributes of the actors within the prediction models can significantly enhance the accuracy and precision of the models.

The main limitation of this research is to investigate the applicability and effectiveness of the proposed model within a single case.

The proposed model can provide a well-established basis for financial intermediaries in SCFs to make more sophisticated decisions within financial facilitation mechanisms.

This study contributes to the existing literature of credit risk evaluation by considering credit risk as a systematic risk that can be influenced by network measures of collaborating actors. To do so, the paper proposes an approach that considers network characteristics of SCFs as critical attributes to predict credit risk.

As foreign direct investment (FDI) plays an important role in economic globalization. This paper examines the structural features of the global FDI network based on FDI flows data and changes in the position of countries within the network.

In order to study the structural characteristics of the global FDI network and the status and changes of countries in the global FDI network, the authors build the investment network and apply the QAP (Quadratic Assignment Procedure) analysis to examine the evolutionary characteristics of the network and its influencing factors.

The global FDI network becomes more interconnected and has a clear “core-periphery” structure. The network connections and volumes have increased dramatically and most countries spread their assets across multiple countries, while only a handful of countries have concentrated investments. The topological structure of the global FDI network has changed noticeably, although this process has been slow and stable and countries in the core position have remained largely intact. The authors find that trade relations between countries, geographic distance and differences in economic size, income levels and institutional environments all have a significant impact on the global FDI network.

Although we find some valuable results, some aspects need further investigation. For example, how a country uses the investment network to boost its economy and how the different industries in the investment network change over time. It is important to get the industry-level details to understand the impact of the global investment network from a government's perspective.

FDI affects the distribution of international capital and contributes to the development of the global economy. Therefore, it is important to study the characteristics of the global FDI network and its development patterns. With more understanding about the network as well as its evolutionary pattern, the government can possibly carry out some policies to promote direct investments as well as economic development.

All countries should actively engage in international direct investments and strengthen their economic ties. At the same time, they can put more emphasis on inward or outward FDI based on their own level of economic development to better establish the circulation channel for domestic and international capital.

This paper examines foreign direct investments through the lens of a global network. In contrast to traditional bilateral studies, this paper focuses on the network structure and evolution, reflecting the dynamics of the entire direct investment system as well as the changing positions of participating countries.

Positive effect of knowledge sharing (KS) on innovation has come to the fore and government-supported innovation and mentoring communities or mentor networks have become widespread. This article aims to examine the community connectedness and mentors' preferences for professional competency-based KS of such innovation community of practice networks (CoPNs).

The paper constructs a directed weighted CoPN model with a node-attribute-based novel fingerprint edge weights. Based on the CoPN, Social Network Analysis (SNA) metrics and measures including Giant Component (GC) were proposed and analyzed to identify mentors' connectedness preferences. The fingerprint was proposed as a novel binarized node attribute of competence. Jaccard similarity of fingerprints was proposed as edge weights to reveal correlations between competences and preferences for KS.

The work opted to conduct a survey of 28 innovation mentors to measure a CoPN. Both a name generator question and a second set of questions were employed to invite respondents to name their collaborators and indicate their professional competence. SNA metrics result in differing values for GC and the rest, which lead us to focus on GC to reveal salient metrics of connectedness. Jaccard similarity analysis results on GC demonstrate that mentors collaborate in an interdisciplinary manner.

Based on the CoPN, the methods proposed may be effective in predicting preferred relationships for interdisciplinary collaborations, providing the managers with an analytical decision support tool for KS in practice.