Titlebook: Statistical Network Analysis: Models, Issues, and New Directions; ICML 2006 Workshop o Edoardo Airoldi,David M. Blei,Alice X. Zheng Confere

终点

Joint Group and Topic Discovery from Relations and Textes and topics among the corresponding text. Block models of relationship data have been studied in social network analysis for some time, however here we cluster in multiple modalities at once. Significantly, joint inference allows the discovery of groups to be guided by the emerging topics, and vic

Admire

Statistical Models for Networks: A Brief Review of Some Recent Researchepresent a network. We use the notation of [1], especially Chapters 13 and 15. There are extensions of these ideas to a wide range of networks, including multiple relations, affiliation relations, valued relations, and social influence and selection situations (in which information on attributes of

ostrish

Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysisety of biological settings, with collections of author-recipient email, and in social networks. Clustering the objects of study or situating them in a low dimensional space (e.g., a simplex) is only one of the goals of the analysis of such data; being able to estimate relational structures among the

Oration

Exploratory Study of a New Model for Evolving Networksic (descriptive) models and are usually restricted to a preset number of people. Moreover, the dynamic aspect is often treated as an addendum to the static model. Taking inspiration from real-life friendship formation patterns, we propose a new generative model of evolving social networks that allow

易改变

Blood-Clot

A Simple Model for Complex Networks with Arbitrary Degree Distribution and Clusteringased solely on their computed degree distribution and clustering coefficient. We propose a statistical model based on these characterizations. This model generalizes models based solely on the degree distribution and is within the curved exponential family class. We present alternative parameterizat

条街道往前推

Discrete Temporal Models of Social NetworksGMs). Many of the methods for ERGMs are readily adapted for these models, including MCMC maximum likelihood estimation algorithms. We discuss models of this type and give examples, as well as a demonstration of their use for hypothesis testing and classification.

嘲笑

Approximate Kalman Filters for Embedding Author-Word Co-occurrence Data over Timesetting. This leads to a non-standard factored state space model with real-valued hidden parent nodes and discrete observation nodes. We investigate the use of variational approximations applied to the observation model that allow us to formulate the entire dynamic model as a Kalman filter. Applying

圆桶

Discovering Functional Communities in Dynamical Networksey can functionally reorganize, even if their underlying anatomical structure remains fixed. However, the recent rapid progress in discovering the community structure of networks has overwhelmingly focused on that constant anatomical connectivity. In this paper, we lay out the problem of discovering

的’

Empirical Analysis of a Dynamic Social Network Built from PGP Keyringsey information. These can be used to build a social network of people connected by email relationships. Since these directories contain creation and expiration time-stamps, the corresponding network can be built and analysed dynamically. At any given point, a snapshot of the current state of the mod