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This chapter provides a deep dive into knowledge networks. The authors provide an inclusive definition of a knowledge network. A knowledge network includes nodes as sources and targets of knowledge, relationships as knowledge links, and messages as knowledge transactions and flows. The authors note how knowledge networks differ from other types of networks, specifically their dynamic and chaotic state and continuous transactions. These peculiarities reflect the economic properties and behaviors of knowledge. The elements of networks described in Chapter 2 are elaborated for knowledge networks. The chapter calls out knowledge network domains, geographies, typologies, nodes, messages, and relationships.

This chapter explores the role of nodes in knowledge networks. The authors characterize knowledge nodes by the type of actors they represent, including individual human agents, collective human groups and teams, explicit non-human objects and resources, and non-human agents and machines. The authors define knowledge nodes by their role in the network, including producer, consumer, or broker of knowledge, and in terms of the stock of knowledge they represent and their capacity to absorb knowledge made available in the network.

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 purpose of this paper is to solve the problem of low efficiency on knowledge resources allocation in the strategic emerging industry (SEI), an incentive model of technology innovation based on knowledge ecological coupling is designed.

First, a principal–agent model of knowledge inputs and a knowledge ecological coupling model based on an improved Lotka–Volterra model are constructed. In addition, a numerical example about Chongqing Yongchuan industrial park, the emulation analysis and the associated discussions are conducted to analyze the equilibriums of principal–agent in different knowledge inputs. Further, the paper analyzes the evolutionary equilibrium in knowledge ecological coupling and reveals the dual adjustments of the node organization on knowledge inputs.

Thus, this paper shows that by establishing the relationships of knowledge ecological coupling based on “mutualism and commensalism,” node organization raises the level of knowledge inputs; an incentive mode of “knowledge ecological coupling relationship + technology innovation chain” is conductive to substantially improving the efficiency of knowledge resource allocation, and to stimulate the vitality of node organization for technology innovation in the strategic emerging industry (SEI).

This paper contributes to the extant researches in two ways. First, this paper reveals the dual adjustments of the node organizations in inputting knowledge, which broadens the vision and borders of the researches on traditional knowledge management. The methods of the traditional principal–agent model and the knowledge input/output profit model are also expanded. Second, this paper verifies that applying the mode of “knowledge ecological coupling relationship + technology innovation chain” in practice is conducive to enhancing the efficiency of the cross-organizational knowledge allocation in the strategic emerging industry (SEI).

This chapter focuses on network links as knowledge flows and relationships. Knowledge links are defined as channels for communicating and distributing knowledge. The literature on network links is aligned with the literature on knowledge sharing, transfer, exchange, and appropriation. This chapter focuses on the peculiar attributes of knowledge network links. The authors identify the attributes to include a link’s direction, length and distance, strength and durability, concentration and congestion, velocity and impact, meaning and intention, and the coverage and spread. The authors also describe standard configurations of knowledge networks.

This chapter focuses on networks comprised of explicit data sources and information and non-human machines as actors. As non-human actors, we include intelligent agents, robotics, and other forms of interactive artificial intelligence. All six facets of knowledge networks are explored. Given these networks’ peculiar nature, three facets have particular importance, including geography, topology, and relationships. The authors provide profiles of seven networks, including semantic and citation networks, webpage networks, communications and computer networks, and energy grids.

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.

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 objective of this paper is to propose a practical and operable method to identify and fill organisational knowledge gaps during new product development.

From a microscopic view, this paper introduces the tree-shaped organisational knowledge structure to formalise the knowledge gaps and their internal hierarchical relationships. Based on the organisational knowledge structure, organisational knowledge gaps are identified through tree matching algorithm. The tree-edit-distance method is introduced to calculate the similarity between two organisational knowledge structures for filling knowledge gap.

The proposed tree-shaped organisational knowledge structure can represent organisations' knowledge and their hierarchy relationships in a structured format, which is useful for identifying and filling organisational knowledge gaps.

The proposed concept of organisational knowledge structure can quantify organisational knowledge. The approach is valuable for strategic decisions regarding new product development. The organisational knowledge gaps identified with this method can provide real-time and accurate guidance for the product development path. More importantly, this method can accelerate the organisational knowledge gap filling process and promote organisational innovation.