Co-authorship networks have been extensively studied in network science as they pose as a perfect example of how single elements of a system give rise to collective phenomena on an intricate, non-trivial structure of interactions. However, co-authorship networks are, in fact, one-mode projections of original bipartite networks, which we call here scientific networks, where authors are agents connected to artifacts - the papers they have published. Nonetheless, few studies take into account the structure of the original bipartite network to understand and explain the topological properties of the projected network. Here, we create bipartite networks using extensive datasets from the American Physical Society (APS) dating back to 1893 up to 2015 inclusive, and from arXiv (1986-2015). We look at the time evolution of publications and at the dynamic structure of scientific networks considering four major features of bipartite networks, namely degree distributions, density, redundancy and cycles. We show how such features shape the formation of co-authorship networks and their observed structural properties. While the structure of the networks of most disciplines does not show significant changes over time, the appearance of large collaborations, in physics, generate largely skewed degree distributions of top nodes. The latter, in turn, induces massive cliques in the projection, triggering considerable densification of the co-authorship network, while the density of the original bipartite network remains at the same levels.
Hierarchical organization of H. Eugene Stanley scientific collaboration community in weighted network representation
通过映射当代社会互动的最先进的元素，世界科学合作网络发展了一个极其复杂的组织。这种异质性的选择特征进行了研究，并确定了重点放在科学合作社区的H。尤金·斯坦利-目前世界上最多产的学者之一。基于截至2016年3月28日的Web of Science记录，构建了几种网络变体。结果发现，斯坦利#1网络-这在类比的Erdos的# -发展了一个很大程度上一致的层次结构和斯坦利自己遵守规则的相同层次结构。然而，这仅在加权网络表示中看到。当这样的加权网络的发展，现有的相关模型表明，权重的传播得到刺激的乘法突发在相邻的节点，这导致它们之间的互连的平衡增长。虽然不是斯坦利独有的，但这种行为并不是一种规则。在这里研究的其他杰出学者的网络更经常发展成一种星状形式，中心枢纽构成了离群点。这项研究的补充频谱分析的归一化拉普拉斯矩阵来自相应的网络的加权变体，除其他外，它指出了这样一个程序的效率，用于确定组件社区和它们之间的关系在复杂的加权网络。
Evaluating Community Detection Algorithms for Progressively Evolving Graphs
Univ de Lyon（里昂大学）；CNRS（法国国家科学研究中心）；Université Lyon 1（里昂第一大学）；Université Mohamed Seddik Benyahia de Jijel（吉杰勒穆罕默德·塞迪克·本叶海亚大学）；Italian National Research Council（意大利国家研究委员会）
