Learning network structures from contagion
نویسندگان
چکیده
منابع مشابه
Learning Network Structures from Contagion
In 2014, Amin, Heidari, and Kearns proved that tree networks can be learned by observing only the infected set of vertices of the contagion process under the independent cascade model, in both the active and passive query models. They also showed empirically that simple extensions of their algorithms work on sparse networks. In this work, we focus on the active model. We prove that a simple mod...
متن کاملLearning from Contagion (Without Timestamps)
We introduce and study new models for learning from contagion processes in a network. A learning algorithm is allowed to either choose or passively observe an initial set of seed infections. This seed set then induces a final set of infections resulting from the underlying stochastic contagion dynamics. Our models differ from prior work in that detailed vertex-byvertex timestamps for the spread...
متن کاملLearning restricted Bayesian network structures
Bayesian networks are basic graphical models, used widely both in statistics and artificial intelligence. These statistical models of conditional independence structure are described by acyclic directed graphs whose nodes correspond to (random) variables in consideration. A quite important topic is the learning of Bayesian network structures, which is determining the best fitting statistical mo...
متن کاملLearning Causal Bayesian Network Structures from Experimental Data
We propose a method for the computational inference of directed acyclic graphical structures given data from experimental interventions. Order-space MCMC, equi-energy sampling, importance weighting and stream-based computation are combined to create a fast algorithm for learning causal Bayesian network structures.
متن کاملLearning Modular Structures from Network Data and Node Variables
A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individuals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, groups are termed modules, and the focus is on estimating the network structu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information Processing Letters
سال: 2017
ISSN: 0020-0190
DOI: 10.1016/j.ipl.2017.01.005