Atelier Visualisation et extraction de connaissances - Irisa

More documents

Recommendations

Info

56 F.Poulet, B.Le Grand : 9e Atelier Visualisation et Extraction de Connaissances
Gephi, an open source software for visualizing networked data Sébastien Heymann LIP6, Université Pierre et Marie Curie - Paris VI 4 place Jussieu, 75252 Paris, France Sebastien.heymann@gmail.com Abstract. Gephi is an open-source network visualization platform. It aims to create a sustainable software and a technical ecosystem driven by an international open-source community, who shares common interests in networks and complex systems. Designed to make data navigation and manipulation easy, it aims to fulfill the complete chain from data importing to aesthetics refinements and interaction. Users interact with the visualization and manipulate structures, shapes and colors to reveal hidden properties. The goal is to help data analysts to make hypothesis, intuitively discover patterns or errors in large data collections. The rendering engine can handle networks larger than 100K elements and guarantees responsiveness. In addition of interactive exploration, Gephi embed most critical metrics used in Social Network Analysis, including Betweenness, Clustering Coefficient, PageRank or Modularity. More metrics can be added thanks to the extensible software architecture and the open-source code. Focus is also made on interoperability, as Gephi can open major file formats, including GraphML, UCINet DL or Pajek. Network results can be exported as PNG, SVG and PDF. Most of current development efforts are made on Dynamic Network Analysis (DNA). Gephi already provides a Timeline component to study network evolutions and visualize changes over time. Created with the idea to be the Photoshop of network visualization, our approach is to provide a visual tool with a smooth learning curve and an active open-source community supporting the project. Many efforts have been made to facilitate the community growth, by providing tutorials, plug-ins development documentation, support and student projects. The modular architecture allows any researcher or developer to extend, reuse and mashup Gephi features in different forms. F.Poulet, B.Le Grand : 9e Atelier Visualisation et Extraction de Connaissances 57
Page 1:
11 e Journées Francophones d'Extra
Page 4 and 5:
Les organisateurs tiennent à remer
Page 7 and 8:
EVARIST: un outil de monitoring du
Page 9 and 10:
E. Cuvelier et M-A. Aufaure FIG. 1
Page 11 and 12: {#ereputation,avis,internautes,70%,
Page 13 and 14: E. Cuvelier et M-A. Aufaure FIG. 5
Page 15 and 16: #ereputation eréputation @laurentb
Page 17 and 18: Concepts > 0.1 , Nuage de Tags en R
Page 19 and 20: Correspondence Analysis for Explora
Page 21 and 22: CA has successfully been used too f
Page 23 and 24: Group 4: “Intraprovince call” S
Page 25 and 26: In this figure, we found that: - Th
Page 27 and 28: Group 3: Customers making local cal
Page 29 and 30: Group 1: customer susing VinaPhone
Page 31 and 32: Identification et visualisation des
Page 33 and 34: Juan David Cruz et al. la sparsité
Page 35 and 36: 3.1 Quelques notations Juan David C
Page 37 and 38: Juan David Cruz et al. La complexit
Page 39 and 40: 5.1 Résultats sur le graphe 1 avec
Page 41 and 42: Juan David Cruz et al. Les algorith
Page 43 and 44: Détection visuelle d’événement
Page 45 and 46: 1.2 Objectif B.Le Grand et M.Latapy
Page 47 and 48: FIG. 2 - Arbre à 2 itérations suc
Page 49 and 50: B.Le Grand et M.Latapy L’ordre de
Page 51 and 52: B.Le Grand et M.Latapy Cette repré
Page 53 and 54: 4 Conclusion et perspectives B.Le G
Page 55 and 56: CUBIST Analytics: A Visual Analysis
Page 57 and 58: C.Melo et M-A.Aufaure tion is based
Page 59 and 60: 4 Open Issues FIG. 3. A “hot box
Page 61: C.Melo et M-A.Aufaure Sarkar, M. &
show all

Atelier Visualisation et extraction de connaissances - Irisa

Create successful ePaper yourself

Delete template?

Save as template?