傳統網路與藝術市場之分析多仰賴靜態之分支度中心性（Degree Centrality），難以捕捉節點影響力之時序動態變化與社群演化軌跡。為解決此拓撲分析在方法上的侷限性，本研究提出一分析框架，將時序知識圖譜（Temporal Knowledge Graph, TKG）定位為底層資料建模基礎。本研究以「臺灣畫廊產業史料庫」等公開資料為基礎，收集並交叉比對 2001 年至 2025 年共 25 年間台灣當代藝術展覽資料，首先建構「畫廊-藝術家」之異質二分圖（Heterogeneous Bipartite Graph），並透過矩陣加權投影（Weighted Projection）轉換為畫廊同質網路（Homogeneous Network）。

在節點影響力分析方面，本系統導入時序 PageRank 演算法，並引入離散時序切片（Discrete Temporal Slices, T1～Tn），量化核心節點間之排名翻轉（Rank Flip）現象。在社群演化方面，本系統整合 Louvain 非監督式社群偵測與 Jaccard 指數映射演算法。考量無尺度網路之長尾特徵，本研究經敏感度分析將演化閾值設定為 θ = 0.05，以追蹤包含延續（Survival）、分裂（Split）、消亡（Death）等狀態之動態社群生命週期。

實驗評估顯示，台灣當代藝術網路之群聚係數為 0.33，且平均最短路徑為 2.7 步，具備顯著的小世界效應（Watts-Strogatz σ ≈ 11.49）與無尺度網路（Scale-free Network）特性。本研究提出之框架量化了市場資訊傳遞之拓撲結構，可作為未來圖譜增強檢索（GraphRAG）與連結預測（Link Prediction）之客觀量化依據。

Most art market network analyses rely on static degree centrality, neglecting the temporal dynamics of node influence and community evolution. To address this, this thesis introduces a dynamic analytical framework using Temporal Knowledge Graphs (TKG). Utilizing a 25-year exhibition dataset (2001-2025) from the Taiwan Art Gallery Archives, we construct a gallery-artist bipartite graph, subsequently transformed into a homogeneous gallery network via weighted matrix projection.

For node influence, discrete temporal slices and the time-dependent PageRank algorithm are incorporated to quantify rank flips among core nodes. Regarding community evolution, the framework integrates the Louvain method with a Jaccard mapping algorithm. An evolution threshold of θ = 0.05 is calibrated to track dynamic lifecycles, identifying states like survival, split, and death.

Empirical evaluations indicate small-world properties (σ ≈ 11.49) and scale-free characteristics, with a clustering coefficient of 0.33 and an average path length of 2.7. The TKG framework establishes a quantitative foundation for strategic resource allocation and future applications like GraphRAG and link prediction.

[1] A. Szabo, “Art market indicators and financial performance,” Journal of Cultural Economics, 2012.
[2] P. Bourdieu, The Field of Cultural Production: Essays on Art and Literature. Columbia University Press, 1993.
[3] L. Manovich and C. Huemer, “Data science and digital art history,”International Journal for Digital Art History, 2017.
[4] A.-L. Barabási, Network Science. Cambridge University Press, 2016.
[5] J. Burkardt, “Network analysis of auction data and cultural collections,” Network Science, 2022.
[6] S. P. Fraiberger, R. Sinatra, M. Resch, C. Riedl, and A.-L. Barabási, “Quantifying reputation and success in art,” Science, vol. 362, no. 6417, pp. 825–829, 2018.
[7] Y. Zhang and X. Liu, “Knowledge graph applications in art history and cultural heritage: A survey,” Digital Scholarship in the Humanities, 2024.
[8] G. Castellano, G. Digeno, A. Sansaro, and G. Vessio, “Knowledge graphs for cultural heritage: A review,” Journal of Cultural Heritage, 2022.
[9] D. Filipiak and A. Filipowska, “Ontology-based approach to art market data analysis,”in International Conference on Business Information Systems, 2016.
[10] Y. Cai, Y. Wang, L. Chen, and V. W. Zheng, “Temporal knowledge graph completion: A survey,” IEEE Transactions on Knowledge and Data Engineering, 2018.
[11] R. Trivedi, H. Dai, Y. Wang, and L. Song, “Know-evolve: Deep temporal knowledge graph embedding,” in Proceedings of the 34th International Conference on Machine Learning, pp. 3462–3471, PMLR, 2017.
[12] L. C. Freeman, “Centrality in social networks conceptual clarification,” Social Networks, vol. 1, no. 3, pp. 215–239, 1978.
[13] M. E. J. Newman, Networks: An Introduction. Oxford University Press, 2010.
[14] R. S. Burt, Structural Holes: The Social Structure of Competition. Harvard University Press, 1992.
[15] R. Flores and M. Romance, “Time-dependent personalized pagerank for temporal networks: Discrete and continuous scales,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 34, no. 8, p. 083145, 2024.
[16] M. E. J. Newman and M. Girvan, “Finding and evaluating community structure in networks,” Physical review E, vol. 69, no. 2, p. 026113, 2004.
[17] H. Liao, M. S. Mariani, M. Medo, Y.-C. Zhang, and T. Zhou, “Ranking in evolving complex networks,” Physics Reports, vol. 689, pp. 1–54, 2017.
[18] L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: Bringing order to the web.,” Tech. Rep. 1999-66, Stanford InfoLab, 1999.
[19] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,” Journal of statistical mechanics: theory and experiment, vol. 2008, no. 10, p. P10008, 2008.
[20] D. Greene, D. Doyle, and P. Cunningham, “Tracking the evolution of communities in dynamic social networks,” in 2010 international conference on advances in social networks analysis and mining, pp. 176–183, IEEE, 2010.
[21] 社團法人中華民國畫廊協會, “臺灣畫廊產業史料庫(taiwan art gallery archives),"2026. Accessed: 2026-07-01.
[22] A.-L. Barabási and R. Albert, “Emergence of scaling in random networks,” Science, vol. 286, no. 5439, pp. 509–512, 1999.
[23] D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small-world’ networks,” Nature, vol. 393, no. 6684, pp. 440–442, 1998.