Add to ORKGInference and optimisation of real-value edge variables in sparse graphs are studied using the tree based Bethe approximation optimisation algorithms. Equilibrium states of general energy functions involving a large set of real edge-variables that interact at the network nodes are obtained for networks in various cases. These include different cost functions, connectivity values, constraints on the edge bandwidth and the case of multiclass optimisation
We study the tailoring of structured random graph ensembles to real networks, with the objective of ...
Abstract. The total communicability of a network (or graph) is defined as the sum of the entries in ...
The problem of designing policies for in-network function computation with minimum energy consumptio...
Inference and optimisation of real-value edge variables in sparse graphs are studied using the tree ...
Inference and optimization of real-value edge variables in sparse graphs are studied using the Bethe...
We apply statistical physics to study the task of resource allocation in random sparse networks with...
Resource allocation in sparsely connected networks, a representative problem of systems with real va...
Attacks upon communication networks have become stealthier and more sophisticated, and hardening com...
We study the equilibrium states of energy functions involving a large set of real variables, defined...
We consider the problem of inference in a graphical model with binary variables. While in theory it ...
Wiener and Randić indices have long been studied in chemical graph theory as connection strength mea...
The problem of resource allocation in sparse graphs with real variables is studied using methods of ...
This valuable source for graduate students and researchers provides a comprehensive introduction to ...
This thesis is concerned with the study of random graphs and random algorithms. There are three over...
We consider network structures that optimize the H2 norm of weighted, time scaled consensus networks...
We study the tailoring of structured random graph ensembles to real networks, with the objective of ...
Abstract. The total communicability of a network (or graph) is defined as the sum of the entries in ...
The problem of designing policies for in-network function computation with minimum energy consumptio...
Inference and optimisation of real-value edge variables in sparse graphs are studied using the tree ...
Inference and optimization of real-value edge variables in sparse graphs are studied using the Bethe...
We apply statistical physics to study the task of resource allocation in random sparse networks with...
Resource allocation in sparsely connected networks, a representative problem of systems with real va...
Attacks upon communication networks have become stealthier and more sophisticated, and hardening com...
We study the equilibrium states of energy functions involving a large set of real variables, defined...
We consider the problem of inference in a graphical model with binary variables. While in theory it ...
Wiener and Randić indices have long been studied in chemical graph theory as connection strength mea...
The problem of resource allocation in sparse graphs with real variables is studied using methods of ...
This valuable source for graduate students and researchers provides a comprehensive introduction to ...
This thesis is concerned with the study of random graphs and random algorithms. There are three over...
We consider network structures that optimize the H2 norm of weighted, time scaled consensus networks...
We study the tailoring of structured random graph ensembles to real networks, with the objective of ...
Abstract. The total communicability of a network (or graph) is defined as the sum of the entries in ...
The problem of designing policies for in-network function computation with minimum energy consumptio...