Previous investigations into creative and innovation networks have suggested that innovations often occurs at the boundary between the network's core and periphery. In this work, we investigate the effect of global core-periphery network structure on the speed and quality of cultural innovation. Drawing on differing notions of core-periphery structure from [arXiv:1808.07801] and [doi:10.1016/S0378-8733(99)00019-2], we distinguish decentralized core-periphery, centralized core-periphery, and affinity network structure. We generate networks of these three classes from stochastic block models (SBMs), and use them to run an agent-based model (ABM) of collective cultural innovation, in which agents can only directly interact with their network neighbors. In order to discover the highest-scoring innovation, agents must discover and combine the highest innovations from two completely parallel technology trees. We find that decentralized core-periphery networks outperform the others by finding the final crossover innovation more quickly on average. We hypothesize that decentralized core-periphery network structure accelerates collective problem-solving by shielding peripheral nodes from the local optima known by the core community at any given time. We then build upon the "Two Truths" hypothesis regarding community structure in spectral graph embeddings, first articulated in [arXiv:1808.07801], which suggests that the adjacency spectral embedding (ASE) captures core-periphery structure, while the Laplacian spectral embedding (LSE) captures affinity. We find that, for core-periphery networks, ASE-based resampling best recreates networks with similar performance on the innovation SBM, compared to LSE-based resampling. Since the Two Truths hypothesis suggests that ASE captures core-periphery structure, this result further supports our hypothesis.

翻译：以往对创意与创新网络的研究表明，创新通常发生在网络核心与边缘的边界区域。本研究探讨全局核心-边缘网络结构对文化创新速度与质量的影响。基于文献[arXiv:1808.07801]与[doi:10.1016/S0378-8733(99)00019-2]中关于核心-边缘结构的不同定义，我们区分了去中心化核心-边缘结构、中心化核心-边缘结构以及亲和网络结构。通过随机块模型（SBM）生成这三类网络，并利用其运行集体文化创新的基于智能体模型（ABM），其中智能体仅能与网络邻居直接交互。为了发现最优创新成果，智能体需从两条完全并行的技术树中探索并组合最高创新。研究发现，去中心化核心-边缘网络在平均速度上更优，能更快找到最终交叉创新。我们假设这种结构通过屏蔽当前核心社区已知的局部最优解，加速了外围节点的集体问题求解过程。进一步基于[arXiv:1808.07801]提出的图谱嵌入中关于社区结构的"双重真理"假说（该假说认为邻接谱嵌入（ASE）捕捉核心-边缘结构，而拉普拉斯谱嵌入（LSE）捕捉亲和特征），我们发现：对于核心-边缘网络，相比LSE重采样方法，基于ASE的重采样能更优地重构出在创新SBM任务中表现相近的网络。由于"双重真理"假说表明ASE捕捉核心-边缘结构，该结果进一步支持了我们的假设。