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Generating random networks efficiently and accurately is an important challenge for practical applications, and an interesting question for theoretical study. This book presents and discusses common methods of generating random graphs. It begins with approaches such as Exponential Random Graph Models, where the targeted probability of each network appearing in the ensemble is specified. This section also includes degree-preserving randomisation algorithms, where the aim is to generate networks with the correct number of links at each node, and care must be taken to avoid introducing a bias. Separately, it looks at growth style algorithms (e.g. preferential attachment) which aim to model a real process and then to analyse the resulting ensemble of graphs. It also covers how to generate special types of graphs including modular graphs, graphs with community structure and temporal graphs. The book is aimed at the graduate student or advanced undergraduate. It includes many worked examples and open questions making it suitable for use in teaching. Explicit pseudocode algorithms are included throughout the book to make the ideas straightforward to apply. With larger and larger datasets, it is crucial to have practical and well-understood tools. Being able to test a hypothesis against a properly specified control case is at the heart of the 'scientific method'. Hence, knowledge on how to generate controlled and unbiased random graph ensembles is vital for anybody wishing to apply network science in their research.

Tailored graph ensembles are a developing bridge between statistical mechanics and biological networks. In this thesis, this concept is used to generate a suite of rigorous mathematical tools to quantify and compare the topology of cellular signalling networks. Earlier published results to quantify the entropy of constrained random graph ensembles are extended by looking at constraints relating to directed graphs, bipartite graphs, neighbourhood compositions and generalised degrees. To incorporate constraints relating to the average number of short loops, a number of innovative techniques are reviewed and extended, moving the analysis beyond the usual tree-like assumption. The generation of unbiased sample networks under some of these new constraints is studied. A series of illustrations of how these concepts may be applied to systems biology are developed. Topological observables are obtained from real biological networks and the entropy of the associated random graph ensemble is calculated. Certain studies on over-represented motifs are replicated and the influence of the choice of null model is considered. The correlation between the topological role of each protein and its lethality is studied in yeast. Throughout, this document aims to promote looking at a network as a realisation satisfying certain constraints rather than just as a list of nodes and edges. This may initially seem to be an abstract approach, but it is in fact a more natural viewpoint within which to consider many fundamental questions regarding the origin, function and design of observed real networks.

Tailored graph ensembles are a developing bridge between statistical mechanics and biological networks. In this thesis, this concept is used to generate a suite of rigorous mathematical tools to quantify and compare the topology of cellular signalling networks. Earlier published results to quantify the entropy of constrained random graph ensembles are extended by looking at constraints relating to directed graphs, bipartite graphs, neighbourhood compositions and generalised degrees. To incorporate constraints relating to the average number of short loops, a number of innovative techniques are reviewed and extended, moving the analysis beyond the usual tree-like assumption. The generation of unbiased sample networks under some of these new constraints is studied. A series of illustrations of how these concepts may be applied to systems biology are developed. Topological observables are obtained from real biological networks and the entropy of the associated random graph ensemble is calculated. Certain studies on over-represented motifs are replicated and the influence of the choice of null model is considered. The correlation between the topological role of each protein and its lethality is studied in yeast. Throughout, this document aims to promote looking at a network as a realisation satisfying certain constraints rather than just as a list of nodes and edges. This may initially seem to be an abstract approach, but it is in fact a more natural viewpoint within which to consider many fundamental questions regarding the origin, function and design of observed real networks.

A fantastic collection of stories of love and intrigue that focus on the trappings of the popular Victorian era, enlivened with fantastical elements and incorporating some noir and detective pieces, by O. M. Grey, Leanna Renee Hieber, N. K. Jemisin, Eliza Knight, Sarah Prineas, Delia Sherman, Genevieve Valentine and many more. Full list of contributors: Vivian Caethe; Leanna Renee Hieber; Seth Cadin; Tiffany Trent; Eliza Knight; Sara Harvey; Rick Bowes; Genevieve Valentine; Nisi Shawl; Maurice Broaddus; Ella D’Arcy; E. Catherine Tobler; Sarah Prineas; Barbara Roden; Mary Braddon; Mae Empson; Caroline Stevermer; Delia Sherman; Tansy Roberts; N. K. Jemisin; O.M. Grey.