Sep 25, 2013 · Abstract:Many real-world processes evolve in cascades over complex networks, whose topologies are often unobservable and change over time.
The method proposed in this thesis is formulated as a linear optimization problem which can be solved efficiently even for large data sets. The model can ...
... network. To infer the network topology, a dynamic structural equation model is adopted that captures the relationship between observed adoption times and ...
Numerical tests with both synthetic and real data demonstrate the effectiveness of the novel algorithms in unveiling sparse dynamically-evolving topologies, ...
To infer the network topology, a dynamic structural equation model is adopted that captures the relationship between observed adoption times and the unknown.
Sep 27, 2013 · To infer the network topology, a dynamic structural equation model is adopted to capture the relationship between observed adoption times and ...
To infer the network topology, a \textit{dynamic} structural equation model is adopted to capture the relationship between observed adoption times and the ...
To infer the network topology, a dynamic structural equation model is adopted that captures the relationship between observed adoption times and the unknown ...
To infer the network topology, a dynamic structural equation model is adopted to capture the relation- ship between observed adoption times and the unknown edge ...
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Dynamic Structural Equation Models for Social Network Topology Inference · Brian BainganaG. MateosG. Giannakis. Computer Science. arXiv.org. 2013. TLDR.