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2013, as the leak by Edward Snowden brought to light, the National Security Agency was engaged in massive-scale network analysis using data from nine internet providers.The study and critique of networks has predominantly taken place within the domains of computer science and related scientific fields, the military, and the tech sector due to the scale of digital data being analysed and the nature of the investigations prompting their study. This book not only argues that arts and humanities scholars can use the same kind of visual and quantitative analysis of networks to shed light on the study of culture; it also contends that the critical skills native to humanistic inquiry are vital to the theorisation and critique of our networked world. Network analysis, as we define it in this book, is a set of practices and discourses that sit at the interface of the natural sciences, humanities, social sciences, computer science, and design. We contend that networks are a category of study that cuts across traditional academic boundaries and that has the potential to unite diverse disciplines through a shared understanding of complexity in our worldwhether that complexity pertains to the nature of the interactions of proteins in gene-regulatory networks or to the network of textual variants that can reveal the lineage of a poem. Moreover, this shared framework provides a compelling case for collaboration across those boundaries, for bringing together computational tools for quantitative network analysis, together with theories, discourses, and applied techniques from the social sciences, the humanities, visual design, and art practice.The cases of Lombardi and Barabási provide an instructive way of grasping that shared framework because, superficially, their work has very little in common. Barabási and Albert explicitly cite the computerisation of data acquisition as essential to their research. By contrast, Lombardi's research process was analogue. He gathered his data on three-by-five notecards. There is no evidence that Lombardi read Barabási and Albert's groundbreaking work in statistics and physics; rather, his inspiration was panorama and history painting. He used the term 'narrative structures' to describe his handdrawn webs of connection. Produced through an iterative process of refinement, the work is human in scale, legible visually in its entirety. Perhaps more importantly, it is his interpretation of a carefully researched but inevitably incomplete record. It does not pretend to objectivity. In stark contrast, Barabási 4Publishing and Book Culture

Developments in AI research have dramatically changed what we can do with data and how we can learn from data. At the same time, implementations of AI amplify the prejudices in data often framed as ‘data bias’ and ‘algorithmic bias.’ Libraries, tasked with deciding what is worth keeping, are inherently discriminatory and yet remain trusted sources of information. As libraries begin to systematically approach their collections as data, will they be able to adopt and adapt the AI-driven tools to traditional practices?   Drawing on the work of the AI initiative within Stanford Libraries, the Fantastic Futures conference on AI for libraries, archives, and museums, and recent scholarship on data bias and algorithmic bias, this article encourages libraries to engage critically with AI and help shape applications of the technology to reflect the ethos of libraries for the benefit of libraries themselves and the patrons they serve. A brief examination of two core concepts in machine learning, generalization and unstructured data, provides points of comparison to library practices in order to uncover the theoretical assumptions driving the different domains. The comparison also offers a point of entry for libraries to adopt machine learning methods on their own terms.

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In this article the authors present an ongoing research project aimed at supporting scholars in the exploration of historical networks through a highly visual and interactive environment for the construction and the manipulation of graphs. They briefly illustrate and discuss a set of techniques defined within a multidisciplinary academic context to better integrate scholars and students’ knowledge beyond the graph.