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In the era of mobile internet, the social Q&A community has built a large-scale and complex knowledge label network through its internal knowledge units, and the scale and structure of the network have changed over time. By analysing the structural characteristics and evolution rules of knowledge label networks, the main purpose of this study is to understand the internal mechanisms of the replacement of old and new knowledge and the expansion of knowledge element boundaries, so as to explore the realization path of knowledge management in the new era from the perspective of complex networks.

This paper uses distributed crawlers to capture 419,349 samples from the Zhihu platform. Each sample contains 33 characteristic dimensions, and the natural year is used as the sliding window to divide the whole. In this study, the global knowledge label network and 11 local knowledge label networks are first constructed. Then, the degree distribution analysis and central node exploration of the knowledge label network are carried out using the complex network method. Finally, the average shortest path and average clustering coefficient of the network are analysed by the time series method, and the ARIMA model is used to predict the evolution of the correlation coefficient.

The research results show that the dissimilation degree of the degree distribution of the knowledge label network has gradually decreased from 2011 to 2021, and the attention of users in the knowledge community has shown a trend of distraction and diversification over time. With the expansion of the scale of the knowledge label network and the transformation to an information network, the network sparsity is becoming more and more obvious, and the knowledge granularity of the Q&A community is being refined and diversified. The prediction of the correlation coefficient of the knowledge label network by the ARIMA model shows that the connection between the labels is lacking diversity and the opinion strengthening phenomenon tends to strengthen, which is more likely to form the “echo chamber effect”, resulting in mutual isolation and even opposition between different circles. The Q&A community is about to enter a mature stage, and the corresponding status of each label has been finalized. The future development trend of label networks will be reflected in the substitution between labels, and the specific structure will not change significantly.

The Q&A community model is the trend in Web 2.0 community development. This study proves the effectiveness of complex networks and time series prediction methods in knowledge label network mining in the Q&A community.

The purpose of this research is to empirically analyse the structure of the Web 2.0 service network and the mechanism behind its evolution over time.

Based on the list of Web 2.0 services and their mashups that is provided on Programmableweb, a network of Web 2.0 services was constructed. Within this network a node represents a Web 2.0 service with an open API, and a link between two nodes represents the existence of a mashup service that uses the two nodes.

The findings suggest that the evolution of the Web 2.0 service network follows the preferential attachment rule although the exponent of the preferential attachment is lower than for other networks following a preferential attachment rule. Additionally the results indicate that the Web 2.0 service network evolves to a scale‐free network but the exponent of the power law distribution is lower than for other networks.

The research applied social network analysis to the Web 2.0 service network. It showed that its network structure and the evolution mechanism are different from those found in similar areas, e.g. the world wide web (WWW). The findings imply that there are factors which lower the exponent of the preferential attachment equation and the power law distribution of the degree centralities.

This paper did not investigate the factors responsible for the low values of the exponent of the preferential attachment equation and the exponent of the power law distribution. However, it is suggested that it could be correlated with the fact that the interconnection between nodes depends on the property of the nodes.

In this chapter we investigate the ownership and control of UK firms using contemporary methods from computational graph theory. Specifically, we analyze a ‘small-world’ model of ownership and control. A small-world is a network whose actors are linked by a short chain of acquaintances (short path lengths), but at the same time have a strongly overlapping circle of friends (high clustering). We simulate a set of corporate worlds using an ensemble of random graphs introduced by Chung and Lu (2002a, 2002b). We find that the corporate governance network structures analyzed here are more clustered (‘clubby’) than would be predicted by the random-graph model. Path lengths, though, are generally not shorter than expected. In addition, we investigate the role of financial institutions: potentially important conduits creating connectivity in corporate networks. We find such institutions give rise to systematically different network topologies.

Most studies on tie strength have focused on its definition, calculation and applications, but have not paid much attention to how tie strength can help analyse online social networks. Because ties play different roles in a network depending on their strength, the purpose of this paper is to explore the relationship between tie strength and network behaviours.

The authors propose a simple metric for tie strength measurement and then apply it to an online social network extracted from a blog network. These networks are massive in size and have technology for efficient data collection, thereby presenting the possibility of measuring tie strength objectively. From the results several key social network properties are studied to see how tie strength may be used as a metric to explain certain characteristics in social networks.

The online networks exhibit all the structural properties of an actual social network, not only in following the power law but also with regard to the distribution of tie strength. The authors noted a strong association between tie strength and reciprocity, and tie strength and transitivity in online social networks.

This paper highlights the importance of analysing online social networks from a tie strength perspective. The results have important implications for the development of efficient search mechanisms and appropriate group leaders in virtual communities.

This paper aims to study the characteristics and evolution rules of tagging knowledge network for users with different activity levels in question-and-answer (Q&A) community represented by Zhihu.

A random sample of issue tag data generated by topics in the Zhihu network environment is selected. By defining user quality and selecting the top 20% and bottom 20% of users to focus on, i.e. top users and bot users, the authors apply time slicing for both types of data to construct label knowledge networks, use Q-Q diagrams and ARIMA models to analyze network indicators and introduce the theory and methods of network motif.

This study shows that when the power index of degree distribution is less than or equal to 3.1, the ARIMA model with rank index of label network has a higher fitting degree. With the development of the community, the correlation between tags in the tagging knowledge network is very weak.

It is not comprehensive and sufficient to classify users only according to their activity levels. And traditional statistical analysis is not applicable to large data sets. In the follow-up work, the authors will further explore the characteristics of the network at a larger scale and longer timescale and consider adding more node features, including some edge features. Then, users are statistically classified according to the attributes of nodes and edges to construct complex networks, and algorithms such as machine learning and deep learning are used to calculate large-scale data sets to deeply study the evolution of knowledge networks.

This paper uses the real data of the Zhihu community to divide users according to user activity and combines the theoretical methods of statistical testing, time series and network motifs to carry out the time series evolution of the knowledge network of the Q&A community. And these research methods provide other network problems with some new ideas. Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates.

Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates. For the community, understanding the formation mechanism of its network structure and key nodes in the network is conducive to improving the knowledge system of the content, finding user behavior preferences and improving user experience. Future research work will focus on identifying outbreak points from a large number of topics, predicting topical trends and conducting timely public opinion guidance and control.

In terms of data selection, the user quality is defined; the Zhihu tags are divided into two categories for time slicing; and network indicators and network motifs are compared and analyzed. In addition, statistical tests, time series analysis and network modality theory are used to analyze the tags.

The research on social media-based academic communication has made great progress with the development of the mobile Internet era, and while a large number of research results have emerged, clarifying the topology of the knowledge label network (KLN) in this field and showing the development of its knowledge labels and related concepts is one of the issues that must be faced. This study aims to discuss the aforementioned issue.

From a bibliometric perspective, 5,217 research papers in this field from CNKI from 2011 to 2021 are selected, and the title and abstract of each paper are subjected to subword processing and topic model analysis, and the extended labels are obtained by taking the merged set with the original keywords, so as to construct a conceptually expanded KLN. At the same time, appropriate time window slicing is performed to observe the temporal evolution of the network topology. Specifically, the basic network topological parameters and the complex modal structure are analyzed empirically to explore the evolution pattern and inner mechanism of the KLN in this domain. In addition, the ARIMA time series prediction model is used to further predict and compare the changing trend of network structure among different disciplines, so as to compare the differences among different disciplines.

The results show that the degree sequence distribution of the KLN is power-law distributed during the growth process, and it performs better in the mature stage of network development, and the network shows more stable scale-free characteristics. At the same time, the network has the characteristics of “short path and high clustering” throughout the time series, which is a typical small-world network. The KLN consists of a small number of hub nodes occupying the core position of the network, while a large number of label nodes are distributed at the periphery of the network and formed around these hub nodes, and its knowledge expansion pattern has a certain retrospective nature. More knowledge label nodes expand from the center to the periphery and have a gradual and stable trend. In addition, there are certain differences between different disciplines, and the research direction or topic of library and information science (LIS) is more refined and deeper than that of journalism and media and computer science. The LIS discipline has shown better development momentum in this field.

KLN is constructed by using extended labels and empirically analyzed by using network frontier conceptual motifs, which reflects the innovation of the study to a certain extent. In future research, the influence of larger-scale network motifs on the structural features and evolutionary mechanisms of KLNs will be further explored.

The potential advantage of extreme value theory in modeling management phenomena is the central theme of this paper. The statistics of extremes have played only a very limited role in management studies despite the disproportionate emphasis on unusual events in the world of managers. An overview of this theory and related statistical models is presented, and illustrative empirical examples provided.

This paper presents a critical review of the economic network analysis methods and their applications to maritime transport. A network can be presented in terms of its structure, topology, characteristics as well as the connectivity with different measures such as density, degree distribution, centrality (degree, betweenness, closeness, eigenvector and strength), clustering coefficient, average shortest path length and assortative. Various models such as the random graph model, block model, and ERGM can be used to analyse and explore the formation of a network and interaction between nodes. The review of the existing theories and models has found that, while these models are rather computationally intensive, they are based on some rather restrictive assumption on network formation and relationship between ports in the network at the local and global levels that require further investigation. Based on the review, a conceptual framework for maritime transport network research is developed, and the applications for future research are also discussed.

This paper presents a critical review of the economic network analysis methods and their applications to maritime transport. A network can be presented in terms of its structure, topology, characteristics as well as the connectivity with different measures such as density, degree distribution, centrality (degree, betweenness, closeness, eigenvector and strength), clustering coefficient, average shortest path length and assortative. Various models such as the random graph model, block model, and ERGM can be used to analyse and explore the formation of a network and interaction between nodes. The review of the existing theories and models has found that, while these models are rather computationally intensive, they are based on some rather restrictive assumption on network formation and relationship between ports in the network at the local and global levels that require further investigation. Based on the review, a conceptual framework for maritime transport network research is developed, and the applications for future research are also discussed.

This study investigated the impact of the coronavirus disease 2019 (COVID-19) pandemic on the Asian-Australasian cruise shipping network. The analysis was carried out using complex network analysis and data collected for two periods, before and after the pandemic outbreak. The analysis revealed that the network structure and properties have changed after the outbreak of the COVID-19 pandemic. Interestingly, the network’s density and the number of links have increased, but its scale-free property remains with the degree distribution follows the power law. The network has a higher connectivity efficiency with a smaller average path length and a higher clustering coefficient. Its hub ports still maintain an extensive connection. The network’s flow efficiency becomes higher and connectivity stronger after the pandemic. The role of cruise ports has changed as indicated by the degree, betweenness, closeness and eigenvector centralities. The study’s findings indicate that the cruise shipping sector could further enhance efficiency and identify strategies to assist the management in similar circumstances.