SlideServe
Télécharger Obtenir Pro
Menu
Accueil Pro
Préférences
ApparenceLight Aide et commentaires
© SlideServe · v2026

Social Network Analysis via Factor Graph Model

dawson avatar
dawson
30 diapositives · 443 Views · Jul 24, 2014

This study explores social network analysis using a factor graph model, focusing on two key methodologies: representative user finding and community discovery. We delve into challenges such as capturing local properties and utilizing edge information. Our experiments, conducted on the Digg.com dataset, evaluate various user behaviors, including submission and interaction patterns. By implementing unsupervised and supervised cases, we uncover insights into community structures and the diffusion of information within social networks. Results demonstrate the identification of prominent users and effective communities, contributing significantly to the understanding of social dynamics.

#social network analysis via factor graph model #social network analysis via factor graph model ppt #powerpoint #presentation
Social Network Analysis via Factor Graph Model - slide 1
01 / 30Social Network Analysis via Factor Graph Model
1 / 17

Télécharger la présentation

Social Network Analysis via Factor Graph Model
Une image / un lien ci-dessous est fourni (tel quel) pour télécharger la présentation

Politique de téléchargement : le contenu du site vous est fourni TEL QUEL pour votre information et votre usage personnel ; il ne peut être vendu, concédé sous licence ni partagé sur d’autres sites sans l’accord de son auteur.Le contenu vous est fourni TEL QUEL, pour votre information et votre usage personnel uniquement. Téléchargez la présentation en cliquant sur ce lien. Lors du téléchargement, si vous ne parvenez pas à télécharger une présentation, l’éditeur a peut-être supprimé le fichier de son serveur.Pendant le téléchargement, si vous ne parvenez pas à obtenir une présentation, le fichier a peut-être été supprimé par l’éditeur.

F I N

Plus de contenu associé

Transcription de la présentation

  1. Social Network Analysis via Factor Graph Model Zi Yang

  2. OUTLINE • Background • Challenge • Unsupervised case 1 • Representative user finding • Unsupervised case 2 • Community discovery • Experiments • Supervised case • Modeling information diffusion in social network

  3. BACKGROUND • Social network • Example: Digg.com • A popular social news website for people to discover and share content • Various types of behaviors of the users • submit, digg, comment and reply a comment • Edges • if one diggs or comments a story of another

  4. BACKGROUND • Community discovery • Modularity property • Affinity propagation • Clustering via factor graph model • Update rules: Pair-wise constrain

  5. BACKGROUND • Affinity propagation Regional constrain Local factor

  6. OUTLINE • Background • Challenge • Unsupervised case 1 • Representative user finding • Unsupervised case 2 • Community discovery • Experiments • Supervised case • Modeling information diffusion in social network

  7. CHALLENGES • How to capture the local properties for social network analysis? • Community discovery as a graph clustering, and how to consider the edge information directly? • Homophily • What constraint can be applied to describe the formation/evolution of community?

  8. OUTLINE • Background • Challenge • Unsupervised case 1 • Representative user finding • Unsupervised case 2 • Community discovery • Experiments • Supervised case • Modeling information diffusion in social network

  9. REPRESENTATIVE USER FINDING • Problem definition • given a social network and (optional) a confidence for each user , the objective is to find a pair-wise representativeness on each edge in the network, and estimate the representative degree of each user in the network, which is denoted by a set of variables satisfying . . In other words, represents the user that mostly trusts (or relies on).

  10. REPRESENTATIVE USER FINDING • Modeling • Input • Variables Represent the representative

  11. REPRESENTATIVE USER FINDING Observation: similarity between the node and variable • Modeling • Node feature function Normalization factor Neighbor Representative Self-representative

  12. REPRESENTATIVE USER FINDING • Modeling • Edge feature function If vertexes of the edge have the same representative Undirected edge: bidirected influence If vertexes of the edge have different representative

  13. REPRESENTATIVE USER FINDING • Modeling • Regional feature function • a feature function defined on the set of neighboring nodes of and itself. To avoid “leader without followers”

  14. REPRESENTATIVE USER FINDING • Modeling • Objective function • Solving • Max-sum algorithm

  15. REPRESENTATIVE USER FINDING • Model learning

  16. REPRESENTATIVE USER FINDING • A bit explanation • : how likely user persuades to take as his representative • : how likely user compliances the suggestion from that he considers as his representative • The direction of such process • Along the directed edges

  17. REPRESENTATIVE USER FINDING • Algorithm

  18. OUTLINE • Background • Challenge • Unsupervised case 1 • Representative user finding • Unsupervised case 2 • Community discovery • Experiments • Supervised case • Modeling information diffusion in social network

  19. COMMUNITY DISCOVERY • Problem definition • given a social network and an expected number of communities , correspondingly a virtual node . is introduced for each community, and the objective is to find a community for each person satisfying , which represents the community that belongs to, such that maximize the preservation of structure (or maximize the modularity of the community).

  20. COMMUNITY DISCOVERY • Feature definition – What’s different? • Node feature function • Edge feature function

  21. COMMUNITY DISCOVERY • Algorithm Result output and Variable updates

  22. OUTLINE • Background • Challenge • Unsupervised case 1 • Representative user finding • Unsupervised case 2 • Community discovery • Experiments • Supervised case • Modeling information diffusion in social network

  23. Experiments • Dataset: Digg.com • a popular social news website for people to discover and share content • 9,583 users, 56,440 contacts • various types of behaviors of the users • submit, digg, comment and reply a comment • Edges (In total: 308,362) • if one diggs or comments a story of another • Weight of the edge: the total number of diggs and comments

  24. Experiments • Dataset: Digg.com • 9,583 users, 56,440 contacts • 308,362 edges • weight of the edge: the total number of diggs and comments • Settings: • Parameter

  25. Experiments • Result: 3 most self-representative users on 3 different topics for Digg user network

  26. Experiments • Result: 3 most representative users of 5 communities on 3 different subset

  27. Experiments • Result: Representative network on a sub graph in Digg-2 Network

  28. OUTLINE • Background • Challenge • Unsupervised case 1 • Representative user finding • Unsupervised case 2 • Community discovery • Experiments • Supervised case • Modeling information diffusion in social network

  29. Modeling information diffusion in social network • Supervised model • Bridging the actual value (label) with the variable. • More variables to come? • Learning the weights

  30. Thanks