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Evolution can be easily observed in nature world, and this phenomenon is a research hotspot no matter in natural science or social science. In crowd science and technology, evolutionary phenomenon exists also among many agents in crowd network systems. This kind of phenomenon is named as crowd co-evolutionary, which cannot be easily studied by most existing methods for its nonlinearity. This paper aims to proposes a novel simulation framework for co-evolution to discover improvements and behaviors of intelligent agents in crowd network systems.

This paper introduces a novel simulation framework for crowd co-evolutions. There are three roles and one scene in the crowd. The scene represented by a band-right to a ringless diagram. The three roles are unit, advisor and monitor. Units find path in the scene. Advisors give advice to units. Monitors supervise units’ behavior in the scene. Building a network among these three kinds member, influencing individual relationships through information exchange, and finally enable the individual to find the optimal path in the scene.

Through this simulation framework, one can record the behavior of an individual in a group, the reasons for the individual's behavior and the changes in the relationships of others in the group that cause the individual to do so. The speed at which an individual finds the optimal path can reflect the advantages and disadvantages of the relationship change function.

The framework provides a new way to study the evolution of inter-individual relationships in crowd networks. This framework takes the first-person perspective of members of the crowd-sourced network as the starting point. Through this framework, the user can design relationship evolution methods and mathematical models for the members of different roles, so as to verify that the level of public intelligence of the crowd network is actually the essence of the rationality of the membership relationship.

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.

Along with the important impact of green strategies on firm survival and performance improvement, its dark side likewise requires attention. By integrating network evolution theory with the literature on green supply chain management, this study proposes a theoretical framework consisting of green strategies, network conduct (cooperation length), network structure (structural holes) and relationship maintenance. The purpose of this study is to indicate how green strategies can affect relationship maintenance on the basis of a network evolution perspective, and demonstrate how this effect can be influenced by cooperation length and structural holes.

Based on a sample of 122 matched pairs of the upstream and downstream supply chain from the Chinese paper-making industry, which is in urgent requirement of green strategies due to high pollution and resource consumption.

This study theoretically and empirically indicates that green strategies may have a positive impact on relationship maintenance. In addition, this effect can be positively moderated by cooperation length, but negatively moderated by structural holes.

This study uncovers the impact of green strategies on relationship maintenance by proposing a network evolution perspective, which could solve its conflicting effects in a specific context and move extant research a step forward.

Corporate philanthropy is increasingly a vital decision-making basis for consumers to purchase and establish relationships with enterprises. However, few studies have examined corporate philanthropy from the perspective of community evolution. To address this gap, this study aims to provide a more in-depth and holistic investigation of corporate philanthropy by examining the evolution of social media brand communities caused by corporate philanthropy and the characteristics of consumer interactive behavior.

Web crawlers developed by Python were employed to collect data of ERKE from Sina Weibo (the Chinese equivalent of Twitter). A total of 2,736 posts and 7,774 comments were collected and investigated using social network and sentiment tendency analyses.

The results showed that the evolution of the social media brand community presented a prominent three-stage characteristic influenced by corporate philanthropy. The findings not only support the benefits of corporate philanthropy but also show the possible disadvantages. Besides, this study further concluded the characteristics of consumer interactive behavior in the social media brand community.

This paper addresses an attractive and practical issue related to the impact of corporate philanthropy. Moreover, this study is one of the first studies to examine the impact of corporate philanthropy in the context of the social media brand community. The findings of this study will provide a valuable reference for community operations and practitioners of brands.

Inter-organizational alliances and the networks they generate have been a central topic in organization theory over the last decade. However, network analyses per se have been static. Even when information over time has been available, the temporal component has been set aside or aggregated to the end point of the study. Substantially more research has been conducted on organizations initiating inter-organizational relationships. The organization-level research has been decidedly dynamic in nature. However, organization-level research has largely examined the structural characteristics of the networks generated by organizational actions. Work combining network-level and organization-level phenomena has been rare and, to our knowledge, no research including the effects of organization-level actions on the evolution of network-level phenomena has occurred.

In this chapter we use more than 6000 R&D alliances and more than 6500 M&D alliances initiated by more than 1000 biotech firms in the U.S. over a 30 year period to construct quarterly networks. We test 13 hypotheses linking the actions of the firms to changes in network structure. Utilizing hazard-rate models we test the effects of institutional status, positional status (centrality), and structural status (coreness) of firms on their propensity to form ties with different structural consequences. Our research indicates that both R&D and M&D networks in U.S. biotechnology are developing a distinct core/periphery structure over time. Furthermore, we find support for a process of preferential attachment wherein organizations are more likely to form ties with organizations of similar institutional and structural status. Furthermore, we find evidence for cross effects, for example attachment processes that enfold across the two networks.

How do ego-networks evolve? How does such evolution affect firms’ innovation output? This chapter uses a longitudinal sample of firms in the biotechnology industry to address these questions. We use social network theory to develop a model of the structure and dynamics of firms’ interorganizational research collaboration ego-networks. Using novel longitudinal methods, this chapter demonstrates how research collaboration ego-networks in the biotechnology industry change over time and how this evolution affects focal firms’ subsequent innovative output. The model is tested on a sample of 482 biotechnology firms over a span of 17 years (1990–2006). The results indicate the significant impacts of ego-network size, ego-network growth, and the inclusion of new members in the ego-network on the innovation output of biotechnology firms. Our results also suggest that enlarging ego-networks by adding new and diverse members presents significant management challenges.

This paper aims to obtain optimal specialization mode and level for complex network or system structures. In the e-commerce system, this paper studies the changes of each transaction subject in the process of ecological structure based on the income level of each transaction subject.

This paper aims to research the change of transaction efficiency evolution process of intermediaries. With the improvement of transaction efficiency, intermediaries interact with other transaction subjects at given modes in e-commerce systems. This paper analyzes the relationship between the factors of production and trade and explains the quantitative relationship between them in the form of mathematical modeling. An evolution simulation framework is established to elaborate the simulation process and method of crowd network in e-commerce ecosystem and then sets up the simulation experiment.

During simulation processes, the changes of data are observed and analyzed to obtain the optimal evolution paths and specialization modes. Furthermore, this paper provides solid supports for the research of the quantitative analysis of ecological structure evolutions.

Evolution simulation of ecological structure is first proposed in the topic of crowd network. It is with the aid of the concept of ecology, the theory and method, simulation of complex network structure and system structure. This paper analyses and researches the evolution process of optimal specialization modes and intelligent level of crowd networks with transaction efficiency changing. The ecological structure optimal evolution paths can be obtained by trend of simulations.

The purpose of this paper is to analyze the evolution of a supply network and the causes of network change and stability. It presents the study of a supply network of Delphi Braga (Portugal), one of the largest European plants producing car-radios, navigation system and other related products, in order to understand how it changed over time. The paper contributes to the understanding of network management.

The case study was carried out on a network of five suppliers of Delphi Braga adopting a relational view of networks meaning that both parties of the relationships were studied. Data and results are based on document sources and interviews with managers both from Delphi and the studied suppliers.

The causes of network change and stability are both exogenous and endogenous. They are multiple and have different impacts on network structures and processes. The paper analyses several causes of network change and their impact on the studied supplier network.

The research is based on a single case study. Although the studied supplier network comprises several firms, it should be pointed out that each network has its own dynamics and structures. Therefore, though the paper makes a relevant contribution for network management, the results cannot be generalized for other networks and firms.

Managers should be aware of the dynamics of their supplier networks and understand the different impacts of both their firm’s decisions as well as the environment changes. By doing this, managers can anticipate changes in the structure and dynamics of their supplier networks.

This case study contributes to our current understanding of relationship development both at the dyadic and network level by analyzing the case of a supply network in a major European plant of car-radios and navigation systems. It discusses implications and proposes further research to advance knowledge on the topic of network change.

In this reply, we respond to a series of substantive comments on our work by both Madhavan and Walker. In our response to Madhavan’s comments, we consider three accounts – “weak,” “semi-strong” and “strong” – that clarify how our model “explains” and offers insights that can emerge from our modeling strategy. We also explore ways in which our explanatory strategy might be extended in light of his critique. In our response to Walker’s comments, we adopt the “semi-strong” thesis, which admits variation in network-generating mechanisms, while also recognizing that information needs to be distributed and shared in order for many types of networks to function.

Destinations have in the scholarly literature been labeled as communities of interdependent organizations that collectively coproduce a variety of products and services. The paradigm comes close to describing destinations as firms which are embedded in interfirm networks. Recent studies provide crucial insights into an understanding of destinations' orchestration and structuration as coproducing interfirm networks. However, systematic knowledge about how these systems evolve and develop is lacking. This chapter addresses this issue and elaborates how the concepts of scale-free and small-world networks together can explain the process of destination evolution. The discussion also suggests how such theorizing can spur avenues for future research.