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Linear System Identification Under Multiplicative Noise from Multiple Trajectory Data
TLDR
The study of multiplicative noise models has a long history in control theory but is re-emerging in the context of complex networked systems and systems with learning-based control. Expand
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Community Detection for Gossip Dynamics with Stubborn Agents
TLDR
We consider a community detection problem for gossip dynamics with stubborn agents in this paper. Expand
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Network Weight Estimation for Binary-Valued Observation Models
TLDR
This paper studies the estimation of network weights for a class of systems with binary-valued observations. Expand
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Detecting Communities in a Gossip Model with Stubborn Agents
TLDR
We consider a community detection problem in a gossip model, where agents randomly interact pairwise, with stubborn agents never changing their states. Expand
Event-Triggered Distributed Estimation With Decaying Communication Rate
TLDR
We study distributed estimation of a high-dimensional static parameter vector through a group of sensors whose communication network is modeled by a fixed directed graph. Expand
A novel opinion model for complex macro-behaviors of mass opinion
Opinion evolution is ubiquitous in everyday life. People often alter their attitudes or behaviors such that they can be more similar to or different from others. These processes of social influenceExpand
A strategic learning algorithm for state-based games
TLDR
We propose a heuristic uncoupled learning algorithm for general state-based games. Expand
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A random opinion formation model over signed networks
  • Yu Xing, H. Fang
  • Computer Science
  • 11th Asian Control Conference (ASCC)
  • 1 December 2017
TLDR
We study a random opinion dynamics model over signed networks with two different topological assumptions, that is, strongly connected and quasi-strongly connected. Expand
A Smooth Bounded Confidence Model Maintaining Clustering Phenomenon
TLDR
We propose a stochastic bounded confidence model which combines DeGroot model with Friedkin-Johnson model as well, by using a bounded confidence framework. Expand
Recursive Network Estimation From Binary-Valued Observation Data
TLDR
This paper studies the problem of recursively estimating the weighted adjacency matrix of a network out of a temporal sequence of binary-valued observations. Expand