Une approche de visualisation analytique pour comparer les modèles de propagation dans les réseaux sociaux

Les modeles de propagation d'informations, d'influence et d'actions dans les reseaux sociaux sont nombreux et diversifies rendant le choix de ce-lui approprie a une situation donnee potentiellement difficile. La selection d'un modele pertinent pour une situation exige de pouvoir effectuer des comparai-sons de modeles. Cette comparaison n'est possible qu'au prix d'une traduction des modeles dans un formalisme commun et independant de ceux-ci. Nous pro-posons l'utilisation de la reecriture de graphes afin d'exprimer les mecanismes de propagation sous la forme d'un ensemble de regles de transformation lo-cales appliquees selon une strategie donnee. Cette demarche prend tout son sens lorsque les modeles ainsi traduits sont etudies et simules a partir d'une plate-forme de visualisation analytique dediee a la reecriture de graphe. Apres avoir decrit quelques modeles et effectue differentes simulations, nous montrons sur quelques exemples comment la plate-forme permet d'interagir avec ces forma-lismes, et comparer interactivement les traces d'execution de chaque modele grâce a diverses mesures soulignant leurs differences.

[1]  Sandra Steinert,et al.  The graph programming language GP , 2007 .

[2]  D. Watts,et al.  A generalized model of social and biological contagion. , 2005, Journal of theoretical biology.

[3]  Wei Chen,et al.  Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.

[4]  M. Macy.  Chains of Cooperation: Threshold Effects in Collective Action , 1991 .

[5]  M. Gatto,et al.  On spatially explicit models of cholera epidemics , 2010, Journal of The Royal Society Interface.

[6]  Albert Zündorf,et al.  The PROGRES approach: language and environment , 1999 .

[7]  John Scott,et al.  The SAGE Handbook of Social Network Analysis , 2011 .

[8]  Mark S. Granovetter.  Threshold Models of Collective Behavior 1 , 2008 .

[9]  Daniel Grund,et al.  GrGen: A Fast SPO-Based Graph Rewriting Tool , 2006, ICGT.

[10]  Wonyeol Lee,et al.  CT-IC: Continuously Activated and Time-Restricted Independent Cascade Model for Viral Marketing , 2012, 2012 IEEE 12th International Conference on Data Mining.

[11]  Hartmut Ehrig,et al.  Handbook of graph grammars and computing by graph transformation: vol. 3: concurrency, parallelism, and distribution , 1999 .

[12]  Duncan J Watts,et al.  A simple model of global cascades on random networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Guy Melançon,et al.  PORGY: A Visual Graph Rewriting Environment for Complex Systems , 2012, Comput. Graph. Forum.

[14]  Éva Tardos,et al.  Influential Nodes in a Diffusion Model for Social Networks , 2005, ICALP.

[15]  Laks V. S. Lakshmanan,et al.  Learning influence probabilities in social networks , 2010, WSDM '10.

[16]  Hartmut Ehrig,et al.  Handbook of graph grammars and computing by graph transformation: vol. 2: applications, languages, and tools , 1999 .

[17]  Arend Rensink.  The GROOVE Simulator: A Tool for State Space Generation , 2003, AGTIVE.

[18]  Detlef Plump,et al.  The Graph Programming Language GP , 2009, CAI.

[19]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[20]  Bruno Courcelle,et al.  Graph Rewriting: An Algebraic and Logic Approach , 1991, Handbook of Theoretical Computer Science, Volume B: Formal Models and Sematics.

[21]  Jure Leskovec,et al.  Inferring networks of diffusion and influence , 2010, KDD.

[22]  Mark E. J. Newman,et al.  Structure and Dynamics of Networks , 2009 .

[23]  S. Wasserman,et al.  Models and Methods in Social Network Analysis: Structural Analysis in the Social Sciences , 2005 .

[24]  Grzegorz Rozenberg,et al.  Handbook of Graph Grammars and Computing by Graph Transformations, Volume 1: Foundations , 1997 .

[25]  Herbert W. Hethcote,et al.  The Mathematics of Infectious Diseases , 2000, SIAM Rev..

[26]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[27]  Lei Wang,et al.  Random pseudofractal scale-free networks with small-world effect , 2006 .

[28]  V. Batagelj,et al.  Probabilistic Inductive Classes of Graphs , 2006, math/0612778.

[29]  Ulrik Brandes,et al.  Analysis and Visualization of Social Networks , 2003, Graph Drawing Software.

[30]  Yifei Yuan,et al.  Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate , 2011, SDM.

[31]  Hélène Kirchner,et al.  Strategic Port Graph Rewriting: An Interactive Modelling and Analysis Framework , 2014, GRAPHITE.