This report discusses measures for link strength in Discrete Bayesian Networks, i.e. measures for the strength of connection along a specific edge. It is a revised version of Report GT-IIC-07-01 (Jan 2007) with improved literature review and explanations. The target application is the visualization of the strengths of the edge connections in a Bayesian Network learned from data to learn more about the inherent properties of the system. The report reviews existing link strength measures, provides an accessible derivation of the primary measure, proposes some simple variations of the primary measure and compares their resulting properties
Social networks often provide only a binary perspective on social ties: two individuals are either c...
A Bayesian network is a graph which features conditional probability tables as edges, and variabl...
Many areas of artificial intelligence must handling with imperfection ofinformation. One of the ways...
This paper discusses measures for connection strength (strength between any two nodes) and link st...
This thesis introduces the concept of a connection strength (CS) between two nodes of a propositiona...
Note: This may or may not be the most recent version of this document. The newest version is always ...
Links play a significant role in the functioning of a complex network. The aim of this thesis is to ...
Abstract—Bayesian Networks are probabilistic models of data that are useful to answer probabilistic ...
The efficiency of algorithms using secondary structures for probabilistic inference in Bayesian netw...
Some structure learning algorithms have proven to be effective in reconstructing hypothetical Bayesi...
Robustness is the ability of networks to avoid malfunction. Networks could be subject to failures, v...
A Bayesian network (BN) is a compact way to represent a joint probability distribution graphically. ...
Causal structure learning algorithms construct Bayesian networks from observational data. Using non-...
In previous work we developed a method of learning Bayesian Network models from raw data. This metho...
Link prediction aims to uncover the underlying relationship behind networks, which could be utilize...
Social networks often provide only a binary perspective on social ties: two individuals are either c...
A Bayesian network is a graph which features conditional probability tables as edges, and variabl...
Many areas of artificial intelligence must handling with imperfection ofinformation. One of the ways...
This paper discusses measures for connection strength (strength between any two nodes) and link st...
This thesis introduces the concept of a connection strength (CS) between two nodes of a propositiona...
Note: This may or may not be the most recent version of this document. The newest version is always ...
Links play a significant role in the functioning of a complex network. The aim of this thesis is to ...
Abstract—Bayesian Networks are probabilistic models of data that are useful to answer probabilistic ...
The efficiency of algorithms using secondary structures for probabilistic inference in Bayesian netw...
Some structure learning algorithms have proven to be effective in reconstructing hypothetical Bayesi...
Robustness is the ability of networks to avoid malfunction. Networks could be subject to failures, v...
A Bayesian network (BN) is a compact way to represent a joint probability distribution graphically. ...
Causal structure learning algorithms construct Bayesian networks from observational data. Using non-...
In previous work we developed a method of learning Bayesian Network models from raw data. This metho...
Link prediction aims to uncover the underlying relationship behind networks, which could be utilize...
Social networks often provide only a binary perspective on social ties: two individuals are either c...
A Bayesian network is a graph which features conditional probability tables as edges, and variabl...
Many areas of artificial intelligence must handling with imperfection ofinformation. One of the ways...