The study of cultural evolutionary patterns, particularly when dealing with artifacts, is constrained by a lack of powerful quantitative methods. In this work, the project team shows that a simple network approach can reconstruct phylogenetic trees from existing databases of recorded artifact influences. They created novel network tools to visualize the large-scale evolution of programming languages. The simple idea of trees of influence can be extended to many other fields beyond the study of programming languages, offering a new theoretical framework to rigorously quantify cultural and technological evolution.

This paper discusses the possibilities of visualizing and analyzing complex and dynamic social networks to understand the interplay between ever-changing social structure and artistic developments within the Antwerp and Brussels tapestry industry (1640–1720).

There is a demand to incorporate content information into social networks. The authors constructed and visualized a network of the most important gods and heroes in the Sanskrit epic Mahābhārata . The network includes semantic information about the actors and their relationships. These two types of information were collected automatically with the help of the Nubbi topic modeling algorithm, which assigns separate sets of topics to both persons and their relations. The visualization of such a network provides intuitive access to a high density of information, like the topic distribution for each actor and the predominant topic for each relation.

Network layouts generated by common algorithms, such as force-directed and spectral methods, are difficult to interpret because the methods are inherently unpredictable. These layouts are ineffective in comparing networks because the layouts lack perceptual uniformity—the differences in output are not proportional to differences in input. To address this, the authors introduce the differential hive plot (DHP), a layout method based on the comparison of two hive plots (HP). The DHP is a difference of two visualizations and contains nodes and edges in the difference or intersection of the two networks based on positional similarity in the input HPs.

This paper addresses two problems: (a) whether and how tools developed to analyze network structures can be applied to the stylometric analysis of texts or text corpora and specifically to authorship attribution problems; and (b) whether it is possible to sample text fragments of an author A so as to imitate the style of an author B. The sample corpora in this study comprise 10–500 English novels from the 19th and 20th centuries.