# Exploring Innovation Ecosystem with Multi-Layered Heterogeneous Networks of Global 5G Communication Technology

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## Abstract

**:**

## 1. Introduction

- What is the holistic IE framework?
- Which innovative actors occupy critical niches in the macro–micro social cooperation network?
- How do knowledge elements in specific niches differ between science and technology sub-ecosystems?
- What is the knowledge proximity among actors and distribution of knowledge areas?

## 2. Literature Review

#### 2.1. Innovation Ecosystem and Its Compositions in Network Perspective

#### 2.2. Multilayer Heterogeneous Network for Innovation and Data

#### 2.3. Key Attributes of Innovation Ecosystems: Ecosystem Niche and Knowledge Proximity

## 3. Theoretical Framework and Methodology

#### 3.1. Social-Knowledge-Science-Technology(A-K-S-T) Ecosystem Framework

#### 3.1.1. A-S Network and A-T Network

#### 3.1.2. K-S Network and K-T Network

#### 3.1.3. A-K-S Network and A-K-T Network

#### 3.2. Network Construction

#### 3.3. Network Measurement

#### 3.4. Data

## 4. Case Study: Worldwide 5G Telecommunication Ecosystem

#### 4.1. Overview: Worldwide 5G Telecommunication Technology

#### 4.2. A-K-S-T Framework of 5G Telecommunication Ecosystem

#### 4.3. Special Ecosystem Niche in 5G Telecommunication Ecosystem

#### 4.3.1. Hub Nodes in Strong Connections

- Degree and degree distribution

- Hub nodes

#### 4.3.2. Bridges of Weak Connections: Bridging Nodes

- Betweenness centrality

- Bridging nodes

#### 4.4. Knowledge Proximity of Social Actors in 5G Telecommunication Ecosystem

#### 4.4.1. Knowledge Distribution in Innovation Actors

#### 4.4.2. Knowledge Proximity of Innovation Actors

## 5. Discussion

## 6. Conclusions

- Strengthening innovation cooperation: Governments and enterprises could encourage enterprises, universities, and individuals to cooperate in emerging technology fields. Complementary advantages promote coordinated development of the innovation ecosystem.
- Establishing a knowledge sharing platform: Governments and enterprises can create a sharing platform to realize the exchange of knowledge, experience, and best practices, and promote learning and cooperation within the innovation ecosystem.
- Determining their position in the innovation collaboration network: Innovators could analyze and understand their position in the collaboration network. By identifying leading innovators and key players, strategic partnerships can be formed to leverage each other’s strengths and focus on breakthrough innovations.
- Harnessing knowledge proximity: High knowledge proximity between entities reduces information transfer costs and barriers. Governments and enterprises should actively explore and utilize knowledge proximity to accelerate innovation and obtain better knowledge resources and cooperation opportunities.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 4.**Knowledge modules of 5G technology (Sources: summarized by authors, adapted from expert interviews, machine learning, and Akyildiz et al.(2016) [84]).

**Figure 8.**Distribution of bridging nodes with the top-10 highlighted in social networks (Note: node size—betweenness centrality), (

**a**) ${\mathrm{G}}_{\mathrm{C}-\mathrm{S}}$; (

**b**) ${\mathrm{G}}_{\mathrm{C}-\mathrm{T}}$; (

**c**) ${\mathrm{G}}_{\mathrm{O}-\mathrm{S}}$; (

**d**) ${\mathrm{G}}_{\mathrm{O}-\mathrm{T}}$; (

**e**) ${\mathrm{G}}_{\mathrm{I}-\mathrm{S}}$; (

**f**) ${\mathrm{G}}_{\mathrm{I}-\mathrm{T}}$.

**Figure 9.**Distribution of top-10 bridging nodes in each knowledge network (Note: node size—betweenness centrality), (

**a**) ${\mathrm{G}}_{\mathrm{K}-\mathrm{S}}$; (

**b**) ${\mathrm{G}}_{\mathrm{K}-\mathrm{T}}$.

**Figure 12.**Clustering of innovation actors: (

**a**) Clustering of countries about scientific knowledge; (

**b**) Clustering of countries about technological knowledge; (

**c**) Clustering of organizations about scientific knowledge; (

**d**) Clustering of organizations about technological knowledge; (

**e**) Clustering of scientists about scientific knowledge; and (

**f**) Clustering of innovators about technological knowledge. (Note: In each subgraph, the clustering distance increases gradually from left to right).

Layer | ${\mathbf{G}}_{\mathbf{N}\mathbf{o}\mathbf{d}\mathbf{e}}$ | Social Actor Cooperation at Macro–Meso–Micro Level | Knowledge Combination | Sample | |||
---|---|---|---|---|---|---|---|

${\mathbf{G}}_{\mathbf{N}\mathbf{e}\mathbf{t}\mathbf{w}\mathbf{o}\mathbf{r}\mathbf{k}}$ | ${\mathbf{V}}_{\mathbf{C}\mathbf{o}\mathbf{u}\mathbf{n}\mathbf{t}\mathbf{r}\mathbf{y}}$ | ${\mathbf{V}}_{\mathbf{O}\mathbf{r}\mathbf{g}\mathbf{a}\mathbf{n}\mathbf{i}\mathbf{z}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n}}$ | ${\mathbf{V}}_{\mathbf{I}\mathbf{n}\mathbf{d}\mathbf{i}\mathbf{v}\mathbf{i}\mathbf{d}\mathbf{u}\mathbf{a}\mathbf{l}}$ | ${\mathbf{V}}_{\mathbf{K}\mathbf{n}\mathbf{o}\mathbf{w}\mathbf{l}\mathbf{e}\mathbf{d}\mathbf{g}\mathbf{e}\mathbf{e}\mathbf{l}\mathbf{e}\mathbf{m}\mathbf{e}\mathbf{n}\mathbf{t}}$ | |||

1st | ${\mathrm{G}}_{\mathrm{A}-\mathrm{S}}$ | ① ${\mathrm{G}}_{\mathrm{C}-\mathrm{S}}$ | ② ${\mathrm{G}}_{\mathrm{O}-\mathrm{S}}$ | ③ ${\mathrm{G}}_{\mathrm{I}-\mathrm{S}}$ | |||

2nd | ${\mathrm{G}}_{\mathrm{K}-\mathrm{S}}$ | ④ ${\mathrm{G}}_{\mathrm{K}-\mathrm{S}}$ | |||||

3rd | ${\mathrm{G}}_{\mathrm{A}-\mathrm{T}}$ | ⑤ ${\mathrm{G}}_{\mathrm{C}-\mathrm{T}}$ | ⑥ ${\mathrm{G}}_{\mathrm{O}-\mathrm{T}}$ | ⑦ ${\mathrm{G}}_{\mathrm{I}-\mathrm{T}}$ | |||

4th | ${\mathrm{G}}_{\mathrm{K}-\mathrm{T}}$ | ⑧ ${\mathrm{G}}_{\mathrm{K}-\mathrm{T}}$ | |||||

1st–2nd | ${\mathrm{G}}_{\mathrm{A}-\mathrm{K}-\mathrm{S}}$ | ⑨ ${\mathrm{G}}_{\mathrm{C}-\mathrm{K}-\mathrm{S}}$ | ⑩ ${\mathrm{G}}_{\mathrm{O}-\mathrm{K}-\mathrm{S}}$ | ⑪ ${\mathrm{G}}_{\mathrm{I}-\mathrm{K}-\mathrm{S}}$ | |||

3rd–4th | ${\mathrm{G}}_{\mathrm{A}-\mathrm{K}-\mathrm{T}}$ | ⑫ ${\mathrm{G}}_{\mathrm{C}-\mathrm{K}-\mathrm{T}}$ | ⑬ ${\mathrm{G}}_{\mathrm{O}-\mathrm{K}-\mathrm{T}}$ | ⑭ ${\mathrm{G}}_{\mathrm{I}-\mathrm{K}-\mathrm{T}}$ |

A-K-S-T Framework | Innovation Feature | Network Metric | Formula | Explanation | Reference |
---|---|---|---|---|---|

Network characteristics | Innovators’ connection distance | Average shortest path and diameter | $\mathrm{L}=\frac{2}{\mathrm{N}\left(\mathrm{N}-1\right)}{\displaystyle \sum _{\mathrm{G}}}{\mathrm{L}}_{{\mathrm{v}}_{\mathrm{i}}{\mathrm{v}}_{\mathrm{j}}}$ $\mathrm{D}=\underset{\mathrm{G}}{\mathrm{max}}{\mathrm{L}}_{{\mathrm{v}}_{\mathrm{i}}{\mathrm{v}}_{\mathrm{j}}}$ | $\mathrm{Shorter}\mathrm{distances},\mathrm{closer}\mathrm{collaboration}.{\mathrm{L}}_{{\mathrm{v}}_{\mathrm{i}}{\mathrm{v}}_{\mathrm{j}}}$$\mathrm{is}\mathrm{shortest}\mathrm{path}\mathrm{length}\mathrm{between}\mathrm{nodes}{\mathrm{v}}_{\mathrm{i}}$$\mathrm{and}{\mathrm{v}}_{\mathrm{j}}$$,\mathrm{N}$$\mathrm{is}\mathrm{node}\mathrm{number}.\mathrm{If}\mathrm{L}\le \mathrm{ln}\mathrm{N}$, it is a small-world network. | [60,61] |

Community stability | Modularity and community quantity | $\mathrm{Q}=\frac{1}{2\mathrm{M}}{\displaystyle \sum _{{\mathrm{v}}_{\mathrm{i}},{\mathrm{v}}_{\mathrm{j}}\in \mathrm{G}}}\left[{\mathrm{A}}_{{\mathrm{v}}_{\mathrm{i}}{\mathrm{v}}_{\mathrm{j}}}-\frac{{\mathrm{d}}_{{\mathrm{v}}_{\mathrm{i}}}{\mathrm{d}}_{{\mathrm{v}}_{\mathrm{j}}}}{2\mathrm{M}}\right]\mathsf{\delta}\left({\mathrm{C}}_{{\mathrm{v}}_{\mathrm{i}}},{\mathrm{C}}_{{\mathrm{v}}_{\mathrm{j}}}\right)$ | $\mathrm{Stronger}\mathrm{community}\mathrm{structure},\mathrm{higher}\mathrm{value}\mathrm{of}\mathrm{modularity}.\mathrm{M}$$\mathrm{is}\mathrm{edge}\mathrm{number},\mathrm{A}$$\mathrm{is}\mathrm{adjacency}\mathrm{matrix}.\mathrm{If}{\mathrm{v}}_{\mathrm{i}}$$\mathrm{and}{\mathrm{v}}_{\mathrm{j}}$$\mathrm{belong}\mathrm{to}\mathrm{same}\mathrm{module},\mathsf{\delta}\left({\mathrm{C}}_{{\mathrm{v}}_{\mathrm{i}}},{\mathrm{C}}_{{\mathrm{v}}_{\mathrm{j}}}\right)=1$$;\mathrm{otherwise},\mathsf{\delta}\left({\mathrm{C}}_{{\mathrm{v}}_{\mathrm{i}}},{\mathrm{C}}_{{\mathrm{v}}_{\mathrm{j}}}\right)=0$. | [62] | |

Cohesion | Clustering coefficient | ${\mathrm{C}}_{{\mathrm{v}}_{\mathrm{i}}}=\frac{2{\mathrm{M}}_{{\mathrm{v}}_{\mathrm{i}}}}{{\mathrm{d}}_{{\mathrm{v}}_{\mathrm{i}}}\left({\mathrm{d}}_{{\mathrm{v}}_{\mathrm{i}}}-1\right)}$ $\mathrm{C}=\frac{1}{\mathrm{N}}{\displaystyle \sum _{{\mathrm{v}}_{\mathrm{i}}\in \mathrm{G}}}{\mathrm{C}}_{{\mathrm{v}}_{\mathrm{i}}}$ | $\mathrm{C}$$\mathrm{relates}\mathrm{the}\mathrm{openness}\mathrm{and}\mathrm{synergies}\mathrm{of}\mathrm{networks}.{\mathrm{M}}_{{\mathrm{v}}_{\mathrm{i}}}$ is actual edge. | [61,63,64] | |

Interaction effectiveness | Global efficiency | ${\mathrm{E}}_{\mathrm{g}\mathrm{l}\mathrm{o}\mathrm{b}}\left(\mathrm{G}\right)=\frac{\sum _{{\mathrm{v}}_{\mathrm{i}}\ne {\mathrm{v}}_{\mathrm{j}}\in \mathrm{G}}{\mathsf{\u03f5}}_{{\mathrm{v}}_{\mathrm{i}}{\mathrm{v}}_{\mathrm{j}}}}{\mathrm{N}\left(\mathrm{N}-1\right)}$ | $\mathrm{High}{\mathrm{E}}_{\mathrm{g}\mathrm{l}\mathrm{o}\mathrm{b}}$$\mathrm{exhibits}\mathrm{high}\mathrm{efficiency}\mathrm{in}\mathrm{global}\mathrm{interaction}.\mathrm{Efficiency}{\mathsf{\u03f5}}_{{\mathrm{v}}_{\mathrm{i}}{\mathrm{v}}_{\mathrm{j}}}=\frac{1}{{\mathrm{L}}_{{\mathrm{v}}_{\mathrm{i}}{\mathrm{v}}_{\mathrm{j}}}}$$,\forall {\mathrm{v}}_{\mathrm{i}},{\mathrm{v}}_{\mathrm{j}}\in \mathrm{G}$. | [30,65,66] | |

Special ecosystem niche | Influence of hub node | Degree and degree distribution | ${\mathrm{d}}_{{\mathrm{v}}_{\mathrm{i}}}=\sum _{{\forall \mathrm{v}}_{\mathrm{j}}\in \mathrm{G}}{\mathrm{x}}_{{\mathrm{v}}_{\mathrm{i}}{\mathrm{v}}_{\mathrm{j}}}$ $\mathrm{f}\left(\mathrm{x}\right)=\mathsf{\lambda}{\mathrm{x}}^{-\mathsf{\alpha}}$ | ${\mathrm{d}}_{{\mathrm{v}}_{\mathrm{i}}}$ represents the importance and centralization of nodes [67]$.\mathrm{Power}-\mathrm{law}\mathrm{exponent}\mathsf{\alpha}$ reveals the cooperative strength of network. | [47,68,69,70] |

Bridging capability of articulation points | Betweenness centrality | $\mathrm{B}\mathrm{C}\left({\mathrm{v}}_{\mathrm{i}}\right)={\displaystyle \sum _{{\mathrm{v}}_{\mathrm{s}},{\mathrm{v}}_{\mathrm{t}}\in \mathrm{G}}}\frac{\mathsf{\sigma}\left({\mathrm{v}}_{\mathrm{s}},{\mathrm{v}}_{\mathrm{t}}|{\mathrm{v}}_{\mathrm{i}}\right)}{\mathsf{\sigma}\left({\mathrm{v}}_{\mathrm{s}},{\mathrm{v}}_{\mathrm{t}}\right)}$ | Bridging nodes controls the flow of non-redundant innovation resource or information [54]$.\mathsf{\sigma}\left({\mathrm{v}}_{\mathrm{s}},{\mathrm{v}}_{\mathrm{t}}\right)$$\mathrm{represents}\mathrm{the}\mathrm{number}\mathrm{of}\mathrm{shortest}\mathrm{paths}\mathrm{passing}\mathrm{through}{\mathrm{v}}_{\mathrm{s}}$$\mathrm{and}{\mathrm{v}}_{\mathrm{t}}$$,\mathrm{and}\mathsf{\sigma}\left({\mathrm{v}}_{\mathrm{s}},{\mathrm{v}}_{\mathrm{t}}|{\mathrm{v}}_{\mathrm{i}}\right)$$\mathrm{is}\mathrm{the}\mathrm{number}\mathrm{of}\mathrm{these}\mathrm{paths}\mathrm{passing}\mathrm{through}{\mathrm{v}}_{\mathrm{i}}$. | [54,60,71,72] | |

Innovators’ knowledge proximity | Knowledge distribution | JS divergence | ${\mathrm{J}\mathrm{S}}_{{\mathrm{v}}_{\mathrm{i}}{\mathrm{v}}_{\mathrm{j}}}\left(\mathrm{p},\mathrm{q}\right)=\frac{1}{2}\left[\mathrm{K}\mathrm{L}\left(\mathrm{p},\frac{\mathrm{p}+\mathrm{q}}{2}\right)+\mathrm{K}\mathrm{L}\left(\mathrm{q},\frac{\mathrm{p}+\mathrm{q}}{2}\right)\right]$ | $\mathrm{Dissimilarity}\mathrm{of}\mathrm{knowledge}\mathrm{domains}\mathrm{evaluates}\mathrm{innovators}\u2019\mathrm{knowledge}\mathrm{focus}\mathrm{distribution}.\mathrm{p}\left({\mathrm{v}}_{\mathrm{i}}\right)$$\mathrm{and}\mathrm{q}\left({\mathrm{v}}_{\mathrm{j}}\right)$$\mathrm{are}\mathrm{probability}\mathrm{densities}\mathrm{of}\mathrm{knowledge}\mathrm{distributions}\mathrm{between}\mathrm{two}\mathrm{innovators}.\mathrm{K}\mathrm{L}\left(\xb7\right)$ is KL distance. | [54,73,74,75,76] |

Knowledge cognitive distance | Euclidean Distance and machine learning. | $\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{t}\left({\mathrm{v}}_{\mathrm{i}},{\mathrm{v}}_{\mathrm{j}}\right)=\sqrt{{\sum}_{\mathrm{w}=1}^{\mathrm{m}}{\left({\mathrm{v}}_{\mathrm{i}\mathrm{w}}-{\mathrm{v}}_{\mathrm{j}\mathrm{w}}\right)}^{2}}$ | $\mathrm{Cognitive}\mathrm{distance}\mathrm{among}\mathrm{innovators}\mathrm{in}\mathrm{knowledge}\mathrm{domains}.\mathrm{Dimension}\mathrm{m}$$\mathrm{is}\mathrm{module}\mathrm{number},\mathrm{w}$$\mathrm{is}\mathrm{the}\mathrm{w}$$\mathrm{th}\mathrm{knowledge}\mathrm{module},{\mathrm{v}}_{\mathrm{i}}$$\mathrm{and}{\mathrm{v}}_{\mathrm{j}}$ are two inventors in same knowledge areas. | [55] |

$\mathbf{G}$ | ${\mathbf{N}}_{\mathbf{G}}$ | ${\mathbf{M}}_{\mathbf{G}}$ | ${\mathbf{D}}_{\mathbf{G}}$ | ${\mathbf{L}}_{\mathbf{G}}$ | ${\mathbf{Q}}_{\mathbf{G}}$ | ${\mathbf{N}}_{\mathbf{c}\mathbf{o}\mathbf{m}}\left(\mathbf{G}\right)$ | ${\mathbf{C}}_{\mathbf{G}}$ | ${\mathbf{E}}_{\mathbf{g}\mathbf{l}\mathbf{o}\mathbf{b}}\left(\mathbf{G}\right)$ | ${\mathbf{G}}_{\mathbf{c}\mathbf{o}\mathbf{n}\mathbf{n}\mathbf{e}\mathbf{c}\mathbf{t}\mathbf{e}\mathbf{d}}$ | |
---|---|---|---|---|---|---|---|---|---|---|

① | ${\mathrm{G}}_{\mathrm{C}-\mathrm{S}}$ | 131 | 1790 | 4 | 1.917 | 0.113 | 5 | 0.776 | 0.5841 | True |

② | ${\mathrm{G}}_{\mathrm{O}-\mathrm{S}}$ | 3535 | 28,061 | 9 | 3.257 | 0.406 | 50 | 0.437 | 0.3133 | False |

③ | ${\mathrm{G}}_{\mathrm{I}-\mathrm{S}}$ | 7758 | 43,157 | 15 | 4.598 | 0.661 | 215 | 0.557 | 0.2003 | False |

④ | ${\mathrm{G}}_{\mathrm{K}-\mathrm{S}}$ | 5356 | 145,997 | 6 | 2.226 | 0.313 | 5 | 0.436 | 0.4663 | False |

⑤ | ${\mathrm{G}}_{\mathrm{C}-\mathrm{T}}$ | 37 | 336 | 3 | 1.505 | 0.080 | 2 | 0.899 | 0.7508 | True |

⑥ | ${\mathrm{G}}_{\mathrm{O}-\mathrm{T}}$ | 334 | 399 | 8 | 2.846 | 0.933 | 81 | 0.691 | 0.0182 | False |

⑦ | ${\mathrm{G}}_{\mathrm{I}-\mathrm{T}}$ | 3172 | 74,554 | 8 | 2.676 | 0.350 | 41 | 0.509 | 0.3901 | False |

⑧ | ${\mathrm{G}}_{\mathrm{K}-\mathrm{T}}$ | 2230 | 60,758 | 7 | 2.641 | 0.454 | 5 | 0.515 | 0.4094 | False |

Ratio | National-Level | Organizational-Level | Individual-Level |
---|---|---|---|

${\mathrm{N}}_{{\mathrm{G}}_{\mathrm{A}-\mathrm{S}}}/{\mathrm{N}}_{{\mathrm{G}}_{\mathrm{A}-\mathrm{T}}}$ | 3.54 | 10.59 | 2.45 |

${\mathrm{M}}_{{\mathrm{G}}_{\mathrm{A}-\mathrm{S}}}/{\mathrm{M}}_{{\mathrm{G}}_{\mathrm{A}-\mathrm{T}}}$ | 5.33 | 70.43 | 0.58 |

$\mathbf{G}$ | ${\mathbf{d}}_{\mathbf{M}\mathbf{e}\mathbf{a}\mathbf{n}}$ | ${\mathbf{d}}_{\mathbf{M}\mathbf{a}\mathbf{x}}$ | ${\mathbf{d}}_{\mathbf{M}\mathbf{i}\mathbf{n}}$ | ${\mathbf{d}}_{\mathbf{M}\mathbf{e}\mathbf{a}\mathbf{d}\mathbf{i}\mathbf{a}\mathbf{n}}$ | ${\mathbf{d}}_{\mathbf{S}.\mathbf{D}.}$ | |
---|---|---|---|---|---|---|

① | ${\mathrm{G}}_{\mathrm{C}-\mathrm{S}}$ | 344.02 | 4614 | 1 | 57 | 724.01 |

② | ${\mathrm{G}}_{\mathrm{O}-\mathrm{S}}$ | 28.31 | 1562 | 1 | 9 | 75.4 |

③ | ${\mathrm{G}}_{\mathrm{I}-\mathrm{S}}$ | 25.80 | 529 | 1 | 16 | 32.42 |

④ | ${\mathrm{G}}_{\mathrm{K}-\mathrm{S}}$ | 99.62 | 30,005 | 1 | 32 | 505.87 |

⑤ | ${\mathrm{G}}_{\mathrm{C}-\mathrm{T}}$ | 3329.83 | 19,522 | 1 | 649 | 5613.09 |

⑥ | ${\mathrm{G}}_{\mathrm{O}-\mathrm{T}}$ | 15.60 | 350 | 1 | 5 | 33.77 |

⑦ | ${\mathrm{G}}_{\mathrm{I}-\mathrm{T}}$ | 95.43 | 2194 | 1 | 40 | 178.28 |

⑧ | ${\mathrm{G}}_{\mathrm{K}-\mathrm{T}}$ | 194.79 | 15,988 | 1 | 40 | 655.96 |

${\mathbf{G}}_{\mathbf{C}-\mathbf{S}}$ | ${\mathbf{G}}_{\mathbf{O}-\mathbf{S}}$ | ${\mathbf{G}}_{\mathbf{I}-\mathbf{S}}$ | ${\mathbf{G}}_{\mathbf{K}-\mathbf{S}}$ | ${\mathbf{G}}_{\mathbf{C}-\mathbf{T}}$ | ${\mathbf{G}}_{\mathbf{O}-\mathbf{T}}$ | ${\mathbf{G}}_{\mathbf{I}-\mathbf{T}}$ | ${\mathbf{G}}_{\mathbf{K}-\mathbf{T}}$ | |
---|---|---|---|---|---|---|---|---|

$\mathsf{\alpha}$ | 2.897 | 2.792 | 3.753 | 2.051 | 9.156 | 3.271 | 2.462 | 3.283 |

Distance | 0.15 | 0.052 | 0.051 | 0.053 | 0.26 | 0.096 | 0.049 | 0.069 |

$\mathbf{G}$ | ${\mathbf{B}\mathbf{C}}_{\mathbf{M}\mathbf{e}\mathbf{a}\mathbf{n}}$ | ${\mathbf{B}\mathbf{C}}_{\mathbf{M}\mathbf{a}\mathbf{x}}$ | ${\mathbf{B}\mathbf{C}}_{\mathbf{M}\mathbf{i}\mathbf{n}}$ | ${\mathbf{B}\mathbf{C}}_{\mathbf{M}\mathbf{e}\mathbf{a}\mathbf{d}\mathbf{i}\mathbf{a}\mathbf{n}}$ | ${\mathbf{B}\mathbf{C}}_{\mathbf{S}.\mathbf{D}.}$ | |
---|---|---|---|---|---|---|

① | ${G}_{C-S}$ | 0.007 | 0.12 | 0 | 0.0005 | 0.018 |

② | ${G}_{O-S}$ | 0.0006 | 0.06 | 0 | 0.00002 | 0.003 |

③ | ${G}_{I-S}$ | 0.0004 | 0.03 | 0 | 0.00005 | 0.001 |

④ | ${G}_{K-S}$ | 0.0002 | 0.41 | 0 | 0.00001 | 0.006 |

⑤ | ${G}_{C-T}$ | 0.014 | 0.17 | 0 | 0.0010 | 0.037 |

⑥ | ${G}_{O-T}$ | 0.0002 | 0.01 | 0 | 0 | 0.001 |

⑦ | ${G}_{I-T}$ | 0.0005 | 0.04 | 0 | 0.00003 | 0.002 |

⑧ | ${G}_{K-T}$ | 0.0007 | 0.11 | 0 | 0.00008 | 0.004 |

$\mathbf{G}$ | $\mathbf{No}.\mathbf{of}{\mathbf{B}\mathbf{C}}_{\mathbf{G}}\left({\mathbf{v}}_{\mathbf{i}}\right)$ | $\mathbf{Percentage}\mathbf{of}{\mathbf{B}\mathbf{C}}_{\mathbf{G}}\left({\mathbf{v}}_{\mathbf{i}}\right)$ | |
---|---|---|---|

① | ${\mathrm{G}}_{\mathrm{C}-\mathrm{S}}$ | 8 | 6.11% |

② | ${\mathrm{G}}_{\mathrm{O}-\mathrm{S}}$ | 355 | 10.04% |

③ | ${\mathrm{G}}_{\mathrm{I}-\mathrm{S}}$ | 382 | 4.92% |

④ | ${\mathrm{G}}_{\mathrm{K}-\mathrm{S}}$ | 7 | 0.13% |

⑤ | ${\mathrm{G}}_{\mathrm{C}-\mathrm{T}}$ | 2 | 5.41% |

⑥ | ${\mathrm{G}}_{\mathrm{O}-\mathrm{T}}$ | 54 | 16.17% |

⑦ | ${\mathrm{G}}_{\mathrm{I}-\mathrm{T}}$ | 48 | 1.51% |

⑧ | ${\mathrm{G}}_{\mathrm{K}-\mathrm{T}}$ | 10 | 0.45% |

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## Share and Cite

**MDPI and ACS Style**

Zhang, X.; Cui, R.; Ji, Y.
Exploring Innovation Ecosystem with Multi-Layered Heterogeneous Networks of Global 5G Communication Technology. *Sustainability* **2024**, *16*, 1380.
https://doi.org/10.3390/su16041380

**AMA Style**

Zhang X, Cui R, Ji Y.
Exploring Innovation Ecosystem with Multi-Layered Heterogeneous Networks of Global 5G Communication Technology. *Sustainability*. 2024; 16(4):1380.
https://doi.org/10.3390/su16041380

**Chicago/Turabian Style**

Zhang, Xiaohang, Ran Cui, and Yajun Ji.
2024. "Exploring Innovation Ecosystem with Multi-Layered Heterogeneous Networks of Global 5G Communication Technology" *Sustainability* 16, no. 4: 1380.
https://doi.org/10.3390/su16041380