Using LTI Dynamics to Identify the Influential Nodes in a Network
Goran Murić1,2, Eduard Jorswieck1, Christian Scheunert1
1Communications Theory, Communications Laboratory, TU Dresden, Saxony, Germany.
Plos One
|December 29, 2016
Summary
We introduce Node Imposed Response (NiR), a new method to identify influential spreaders in networks. NiR accurately measures node spreading power, outperforming traditional centrality measures for network analysis.
Area of Science:
- Network Science
- Systems Theory
- Computational Social Science
Background:
- Networks model diverse systems (technical, social, biological).
- Identifying key nodes is crucial for understanding system dynamics and resource allocation.
- Influence and spreading capability are key node attributes.
Purpose of the Study:
- To propose a novel measure, Node Imposed Response (NiR), for evaluating node spreading power.
- To compare NiR's accuracy against established centrality metrics.
- To provide a robust tool for network analysis and protective strategies.
Main Methods:
- Utilizing a system-theoretic approach, modeling networks as Linear Time-Invariant (LTI) systems.
- Quantifying node importance by observing the system's response.
- Benchmarking NiR against betweenness, degree, k-shell, and h-index centrality.
Main Results:
- Node Imposed Response (NiR) accurately evaluates node spreading power.
- NiR demonstrates superior performance compared to betweenness, degree, k-shell, and h-index centrality in many scenarios.
- NiR achieves accuracy comparable to dynamics-sensitive centrality measures.
Conclusions:
- NiR offers a precise and effective method for identifying influential nodes in networks.
- The system-theoretic approach provides a robust framework for network analysis.
- NiR enhances strategies for resource management, information dissemination, and network protection.
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