Explosive Growth in Large-Scale Collaboration Networks
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arXiv
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| Format: | Preprint |
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2025
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| author | Williams, Peter Chen, Zhan |
| author_facet | Williams, Peter Chen, Zhan |
| contents | We analyse the evolution of two large collaboration networks: the Microsoft Academic Graph (1800-2020) and Internet Movie Database (1900-2020), comprising $2.72 \times 10^8$ and $1.88 \times 10^6$ nodes respectively. The networks show super-linear growth, with node counts following power laws $N(t) \propto t^α$ where $α= 2.3$ increasing to $3.1$ after 1950 (MAG) and $α= 1.8$ (IMDb). Node and edge processes maintain stable but noisy timescale ratios ($τ_N/τ_E \approx 2.8 \pm 0.3$ MAG, $2.3 \pm 0.2$ IMDb). The probability of waiting a time $t$ between successive collaborations was found to be scale-free, $P(t) \propto t^{-γ}$, with indices evolving from $γ\approx 2.3$ to $1.6$ (MAG) and $2.6$ to $2.1$ (IMDb). Academic collaboration sizes increased from $1.2$ to $5.8$ authors per paper, while entertainment collaborations remained more stable ($3.2$ to $4.5$ actors). These observations indicate that current network models might be enhanced by considering accelerating growth, coupled timescales, and environmental influence, while explaining stable local properties. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_11109 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explosive Growth in Large-Scale Collaboration Networks Williams, Peter Chen, Zhan Social and Information Networks Physics and Society We analyse the evolution of two large collaboration networks: the Microsoft Academic Graph (1800-2020) and Internet Movie Database (1900-2020), comprising $2.72 \times 10^8$ and $1.88 \times 10^6$ nodes respectively. The networks show super-linear growth, with node counts following power laws $N(t) \propto t^α$ where $α= 2.3$ increasing to $3.1$ after 1950 (MAG) and $α= 1.8$ (IMDb). Node and edge processes maintain stable but noisy timescale ratios ($τ_N/τ_E \approx 2.8 \pm 0.3$ MAG, $2.3 \pm 0.2$ IMDb). The probability of waiting a time $t$ between successive collaborations was found to be scale-free, $P(t) \propto t^{-γ}$, with indices evolving from $γ\approx 2.3$ to $1.6$ (MAG) and $2.6$ to $2.1$ (IMDb). Academic collaboration sizes increased from $1.2$ to $5.8$ authors per paper, while entertainment collaborations remained more stable ($3.2$ to $4.5$ actors). These observations indicate that current network models might be enhanced by considering accelerating growth, coupled timescales, and environmental influence, while explaining stable local properties. |
| title | Explosive Growth in Large-Scale Collaboration Networks |
| topic | Social and Information Networks Physics and Society |
| url | https://arxiv.org/abs/2502.11109 |