The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Alessandro Lomi - One of the best experts on this subject based on the ideXlab platform.
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exponential random graph Model Parameter estimation for very large directed networks
PLOS ONE, 2020Co-Authors: Alex Stivala, Garry Robins, Alessandro LomiAbstract:Exponential random graph Models (ERGMs) are widely used for Modeling social networks observed at one point in time. However the computational difficulty of ERGM Parameter estimation has limited the practical application of this class of Models to relatively small networks, up to a few thousand nodes at most, with usually only a few hundred nodes or fewer. In the case of undirected networks, snowball sampling can be used to find ERGM Parameter estimates of larger networks via network samples, and recently published improvements in ERGM network distribution sampling and ERGM estimation algorithms have allowed ERGM Parameter estimates of undirected networks with over one hundred thousand nodes to be made. However the implementations of these algorithms to date have been limited in their scalability, and also restricted to undirected networks. Here we describe an implementation of the recently published Equilibrium Expectation (EE) algorithm for ERGM Parameter estimation of large directed networks. We test it on some simulated networks, and demonstrate its application to an online social network with over 1.6 million nodes.
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exponential random graph Model Parameter estimation for very large directed networks
PLOS ONE, 2020Co-Authors: Alex Stivala, Garry Robins, Alessandro LomiAbstract:: Exponential random graph Models (ERGMs) are widely used for Modeling social networks observed at one point in time. However the computational difficulty of ERGM Parameter estimation has limited the practical application of this class of Models to relatively small networks, up to a few thousand nodes at most, with usually only a few hundred nodes or fewer. In the case of undirected networks, snowball sampling can be used to find ERGM Parameter estimates of larger networks via network samples, and recently published improvements in ERGM network distribution sampling and ERGM estimation algorithms have allowed ERGM Parameter estimates of undirected networks with over one hundred thousand nodes to be made. However the implementations of these algorithms to date have been limited in their scalability, and also restricted to undirected networks. Here we describe an implementation of the recently published Equilibrium Expectation (EE) algorithm for ERGM Parameter estimation of large directed networks. We test it on some simulated networks, and demonstrate its application to an online social network with over 1.6 million nodes.
Alex Stivala - One of the best experts on this subject based on the ideXlab platform.
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exponential random graph Model Parameter estimation for very large directed networks
PLOS ONE, 2020Co-Authors: Alex Stivala, Garry Robins, Alessandro LomiAbstract:Exponential random graph Models (ERGMs) are widely used for Modeling social networks observed at one point in time. However the computational difficulty of ERGM Parameter estimation has limited the practical application of this class of Models to relatively small networks, up to a few thousand nodes at most, with usually only a few hundred nodes or fewer. In the case of undirected networks, snowball sampling can be used to find ERGM Parameter estimates of larger networks via network samples, and recently published improvements in ERGM network distribution sampling and ERGM estimation algorithms have allowed ERGM Parameter estimates of undirected networks with over one hundred thousand nodes to be made. However the implementations of these algorithms to date have been limited in their scalability, and also restricted to undirected networks. Here we describe an implementation of the recently published Equilibrium Expectation (EE) algorithm for ERGM Parameter estimation of large directed networks. We test it on some simulated networks, and demonstrate its application to an online social network with over 1.6 million nodes.
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exponential random graph Model Parameter estimation for very large directed networks
PLOS ONE, 2020Co-Authors: Alex Stivala, Garry Robins, Alessandro LomiAbstract:: Exponential random graph Models (ERGMs) are widely used for Modeling social networks observed at one point in time. However the computational difficulty of ERGM Parameter estimation has limited the practical application of this class of Models to relatively small networks, up to a few thousand nodes at most, with usually only a few hundred nodes or fewer. In the case of undirected networks, snowball sampling can be used to find ERGM Parameter estimates of larger networks via network samples, and recently published improvements in ERGM network distribution sampling and ERGM estimation algorithms have allowed ERGM Parameter estimates of undirected networks with over one hundred thousand nodes to be made. However the implementations of these algorithms to date have been limited in their scalability, and also restricted to undirected networks. Here we describe an implementation of the recently published Equilibrium Expectation (EE) algorithm for ERGM Parameter estimation of large directed networks. We test it on some simulated networks, and demonstrate its application to an online social network with over 1.6 million nodes.
Afshin Izadian - One of the best experts on this subject based on the ideXlab platform.
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electrochemical Model Parameter identification of a lithium ion battery using particle swarm optimization method
Journal of Power Sources, 2016Co-Authors: Ashiqur Rahman, Sohel Anwar, Afshin IzadianAbstract:Abstract In this paper, a gradient-free optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized to identify specific Parameters of the electrochemical Model of a Lithium-Ion battery with LiCoO 2 cathode chemistry. Battery electrochemical Model Parameters are subject to change under severe or abusive operating conditions resulting in, for example, over-discharged battery, over-charged battery, etc. It is important for a battery management system to have these Parameter changes fully captured in a bank of battery Models that can be used to monitor battery conditions in real time. Here the PSO methodology has been successfully applied to identify four electrochemical Model Parameters that exhibit significant variations under severe operating conditions: solid phase diffusion coefficient at the positive electrode (cathode), solid phase diffusion coefficient at the negative electrode (anode), intercalation/de-intercalation reaction rate at the cathode, and intercalation/de-intercalation reaction rate at the anode. The identified Model Parameters were used to generate the respective battery Models for both healthy and degraded batteries. These Models were then validated by comparing the Model output voltage with the experimental output voltage for the stated operating conditions. The identified Li-Ion battery electrochemical Model Parameters are within reasonable accuracy as evidenced by the experimental validation results.
Garry Robins - One of the best experts on this subject based on the ideXlab platform.
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exponential random graph Model Parameter estimation for very large directed networks
PLOS ONE, 2020Co-Authors: Alex Stivala, Garry Robins, Alessandro LomiAbstract:Exponential random graph Models (ERGMs) are widely used for Modeling social networks observed at one point in time. However the computational difficulty of ERGM Parameter estimation has limited the practical application of this class of Models to relatively small networks, up to a few thousand nodes at most, with usually only a few hundred nodes or fewer. In the case of undirected networks, snowball sampling can be used to find ERGM Parameter estimates of larger networks via network samples, and recently published improvements in ERGM network distribution sampling and ERGM estimation algorithms have allowed ERGM Parameter estimates of undirected networks with over one hundred thousand nodes to be made. However the implementations of these algorithms to date have been limited in their scalability, and also restricted to undirected networks. Here we describe an implementation of the recently published Equilibrium Expectation (EE) algorithm for ERGM Parameter estimation of large directed networks. We test it on some simulated networks, and demonstrate its application to an online social network with over 1.6 million nodes.
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exponential random graph Model Parameter estimation for very large directed networks
PLOS ONE, 2020Co-Authors: Alex Stivala, Garry Robins, Alessandro LomiAbstract:: Exponential random graph Models (ERGMs) are widely used for Modeling social networks observed at one point in time. However the computational difficulty of ERGM Parameter estimation has limited the practical application of this class of Models to relatively small networks, up to a few thousand nodes at most, with usually only a few hundred nodes or fewer. In the case of undirected networks, snowball sampling can be used to find ERGM Parameter estimates of larger networks via network samples, and recently published improvements in ERGM network distribution sampling and ERGM estimation algorithms have allowed ERGM Parameter estimates of undirected networks with over one hundred thousand nodes to be made. However the implementations of these algorithms to date have been limited in their scalability, and also restricted to undirected networks. Here we describe an implementation of the recently published Equilibrium Expectation (EE) algorithm for ERGM Parameter estimation of large directed networks. We test it on some simulated networks, and demonstrate its application to an online social network with over 1.6 million nodes.
Ashiqur Rahman - One of the best experts on this subject based on the ideXlab platform.
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electrochemical Model Parameter identification of a lithium ion battery using particle swarm optimization method
Journal of Power Sources, 2016Co-Authors: Ashiqur Rahman, Sohel Anwar, Afshin IzadianAbstract:Abstract In this paper, a gradient-free optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized to identify specific Parameters of the electrochemical Model of a Lithium-Ion battery with LiCoO 2 cathode chemistry. Battery electrochemical Model Parameters are subject to change under severe or abusive operating conditions resulting in, for example, over-discharged battery, over-charged battery, etc. It is important for a battery management system to have these Parameter changes fully captured in a bank of battery Models that can be used to monitor battery conditions in real time. Here the PSO methodology has been successfully applied to identify four electrochemical Model Parameters that exhibit significant variations under severe operating conditions: solid phase diffusion coefficient at the positive electrode (cathode), solid phase diffusion coefficient at the negative electrode (anode), intercalation/de-intercalation reaction rate at the cathode, and intercalation/de-intercalation reaction rate at the anode. The identified Model Parameters were used to generate the respective battery Models for both healthy and degraded batteries. These Models were then validated by comparing the Model output voltage with the experimental output voltage for the stated operating conditions. The identified Li-Ion battery electrochemical Model Parameters are within reasonable accuracy as evidenced by the experimental validation results.
