A social network analysis of Rousseau’s autobiography “Les Confessions”

We propose an analysis of the social network composed of the characters appearing in Jean-Jacques Rousseau's autobiographic Les Confessions, with existence of edges based on co-occurrences. This work consists of twelve volumes, that span over fifty years of his life. Having a unique author allows us to consider the book as a coherent work, unlike some of the historical texts from which networks often get extracted, and to compare the evolution of patterns of characters through the books on a common basis. Les Confessions, considered as one of the first modern autobiographies, has the originality to let us compose a social network close to the reality, only with a bias introduced by the author, that has to be taken into account during the analysis. Hence, with this paper, we discuss the interpretation of networks based on the content of a book as social networks. We also, in a digital humanities approach, discuss the relevance of this object as an historical source and a narrative tool.

Character Networks and Centrality

Yannick Rochat

A character network represents relations between characters from a text; the relations are based on text proximity, shared scenes/events, quoted speech, etc. Our project sketches a theoretical framework for character network analysis, bringing together narratology, both close and distant reading approaches, and social network analysis. It is in line with recent attempts to automatise the extraction of literary social networks (Elson, 2012; Sack, 2013) and other studies stressing the importance of character- systems (Woloch, 2003; Moretti, 2011). The method we use to build the network is direct and simple. First, we extract co-occurrences from a book index, without the need for text analysis. We then describe the narrative roles of the characters, which we deduce from their respective positions in the network, i.e. the discourse. As a case study, we use the autobiographical novel Les Confessions by Jean-Jacques Rousseau. We start by identifying co-occurrences of characters in the book index of our edition (Slatkine, 2012). Subsequently, we compute four types of centrality: degree, closeness, betweenness, eigenvector. We then use these measures to propose a typology of narrative roles for the characters. We show that the two parts of Les Confessions, written years apart, are structured around mirroring central figures that bear similar centrality scores. The first part revolves around the mentor of Rousseau; a figure of openness. The second part centres on a group of schemers, depicting a period of deep paranoia. We also highlight characters with intermediary roles: they provide narrative links between the societies in the life of the author. The method we detail in this complete case study of character network analysis can be applied to any work documented by an index.

University of Lausanne2014

From recommender systems to spatio-temporal dynamics with network science

Kirell Maël Benzi

Networks are data structures that are fundamental for capturing and analyzing complex interactions between objects. While they have been used for decades to solve problems in virtually all scientific fields, their usage for data analysis in real-world practical applications deserves to be further investigated. In this thesis, we explore multiple aspects of network science and show how the design of new graph-based approaches offers an unprecedented depth for analyzing complex datasets. Through the study of practical applications, we demonstrate how to extract key findings in several domains such as digital humanities, recommender systems, social behavior, neuroscience or knowledge discovery. First, we propose to define in a concise manner the data science workflow. We present the tools, techniques, and questions that the practitioner needs to have in mind when addressing a new large-scale problem as they are of tremendous importance if one wants to apply network science concepts to real applications. Based on this foundation chapter, we begin by demonstrating the worth of networks for music recommendation with Genezik, our smart playlist application that adapts to user taste. Using signal processing, machine learning, and graphs, we show how to improve the performance of recommender systems as well as proposing a radically different user experience that has yet to be found in competing systems. We then move on to the introduction of the causal multilayer graph of activity, a novel graph method dedicated to the analysis of dynamical processes over networks. More than a data structure, we present a data analysis approach that tracks spreading or propagation of events through time in a scalable manner by efficiently combining a network with values associated with its vertices. Used in four different applications, the analysis of spatio-temporal patterns of activity extracted from the causal multilayer graph helps us better understand how rumors spread in social networks or how brain regions interact in resting states for instance. Finally, we study the browsing behavior of millions of people on Wikipedia and show how to extract contextual patterns of activity that reflect what is collectively remembered from past events. Based on their analysis, we confirm social studies on human behavior and conclude by revealing some of the rules that define human curiosity.

EPFL2017

From recommender systems to spatio-temporal dynamics with network science

Kirell Maël Benzi

EPFL2017