Aux sources des grands réseaux d'interactions

Cristofoli, Pascal

doi:10.3917/res.152.0021

Cited by 5 publications

(4 citation statements)

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“…Still, the network preserves the reality of the social relationships mentioned in the sources. More precisely, Cristofoli demonstrates that bipartite networks ensure no distortion or ambiguity, unlike projected networks when modeling textual sources [9]. Furthermore, when attributes are encoded, projected networks require the duplication of information related to events.…”

Section: Bipartite Multivariate Dynamic Social Networkmentioning

confidence: 99%

From Historical Documents To Social Network Visualization: Potential Pitfalls and Network Modeling

Pister¹,

Dufournaud

Cristofoli

et al. 2022

2022 IEEE 7th Workshop on Visualization for the Digital Humanities (VIS4DH)

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We describe the workflow followed by historians when conducting a Historical Social Network Analysis (HSNA) with five steps: textual sources acquisition, digitization, annotation, network creation, and analysis/visualization. While most analysis and visualization tools only support the last step, we argue that addressing the 2-3 last steps would boost the humanists' analytical capabilities. We explain why the network modeling process is particularly challenging and can lead to distortions of the sources, biases, and traceability problems. We list three main properties that we believe the constructed network should satisfy: alignment with reality/documents (not only with concepts), traceability (from documents to analysis/visualization and back), and simplicity (understandable by most and not more complex than needed). We claim that the model of bipartite dynamic multivariate network with roles allows an effective annotation/encoding of historical sources while satisfying these properties. We provide real-world examples of how this model has been used to answer socio-historical questions using visual analytics tools.

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Section: Bipartite Multivariate Dynamic Social Networkmentioning

confidence: 99%

From Historical Documents To Social Network Visualization: Potential Pitfalls and Network Modeling

Pister¹,

Dufournaud

Cristofoli

et al. 2022

2022 IEEE 7th Workshop on Visualization for the Digital Humanities (VIS4DH)

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“…Dans cet article, nous n'en retiendrons que quelques-unes, tout en rappelant qu'il en existe d'autres. Ainsi, la taille d'un réseau représente le nombre de relations qu'a l'individu, directes ou indirectes (Lin 1995 ;Cristofoli 2008). Même s'il est extrêmement délicat de définir la taille objective d'un réseau social (Langlois 1977), elle a l'avantage d'être aisée à concevoir et de permettre des comparaisons entre réseaux.…”

Section: Caractéristiques Des Réseauxunclassified

Le réseau social, un outil inégalitaire ?

Turtschi¹

2016

Swiss Journal of Sociology

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RésuméeLe réseau social d’un individu est souvent considéré comme un élément fondamental pour sa réinsertion professionnelle. Cependant, le réseau ne se développe pas ex nihilo mais, comme montré dans l’article, en fonction des caractéristiques sociodémographiques de l’individu. De plus, les résultats indiquent que ce sont les personnes les moins vulnérables sur le marché de l’emploi qui disposent également des caractéristiques nécessaires à la constitution d’un réseau efficace pour l’insertion professionnelle, ce qui accroît encore les inégalités entre elles et les plus vulnérables.

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“…These are often essential to understanding underlying social phenomena, as time, space and social status play an important role in social dynamics. For these reasons, historical sources and the underlying social phenomena they refer to can be modelled well by bipartite , multivariate dynamic networks with roles [PDC*22, Cri08]. Bipartite means that both persons and documents (or events that are often witnessed by physical documents) are modelled as typed nodes.…”

Section: Introductionmentioning

confidence: 99%

ComBiNet: Visual Query and Comparison of Bipartite Multivariate Dynamic Social Networks

Pister

Prieur

Fekete

2023

Computer Graphics Forum

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We present ComBiNet, a visualization, query, and comparison system for exploring bipartite multivariate dynamic social networks. Historians and sociologists study social networks constructed from textual sources mentioning events related to people, such as marriage acts, birth certificates and contracts. We model this type of data using bipartite multivariate dynamic networks to maintain a representation faithful to the original sources while not too complex. Relying on this data model, ComBiNet allows exploring networks using both visual and textual queries using the Cypher language, the two being synchronized to specify queries using the most suitable modality; simple queries are easy to express visually and can be refined textually when they become complex. These queries are used for applying topological and attribute‐based selection on the network. Query results are visualized in the context of the whole network and over a geographical map for geolocalized entities. We also present the design of our interaction techniques for querying social networks to visually compare the selections in terms of topology, measures and attribute distributions. We validate the query and comparison systems by showing how they have been used to answer historical questions and by explaining how they have been improved through a usability study conducted with historians.

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Aux sources des grands réseaux d'interactions

Cited by 5 publications

References 0 publications

From Historical Documents To Social Network Visualization: Potential Pitfalls and Network Modeling

From Historical Documents To Social Network Visualization: Potential Pitfalls and Network Modeling

Le réseau social, un outil inégalitaire ?

ComBiNet: Visual Query and Comparison of Bipartite Multivariate Dynamic Social Networks

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