Maximizing Algebraic Connectivity in the Space of Graphs With a Fixed Number of Vertices and Edges
The second smallest eigenvalue of the Laplacian matrix, also known as the algebraic connectivity, characterizes the performance of some dynamic processes on networks, such as consensus in multiagent networks, synchronization of coupled oscillators, random walks on graphs, and so on. In a multiagent...
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| Veröffentlicht in: | IEEE transactions on control of network systems 2017-06, Vol.4 (2), p.359-368 |
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| creator | Ogiwara, Kohnosuke Fukami, Tatsuya Takahashi, Norikazu |
| description | The second smallest eigenvalue of the Laplacian matrix, also known as the algebraic connectivity, characterizes the performance of some dynamic processes on networks, such as consensus in multiagent networks, synchronization of coupled oscillators, random walks on graphs, and so on. In a multiagent network, for example, the larger the algebraic connectivity of the graph representing interactions between agents is, the faster the convergence speed of a representative consensus algorithm is. This paper tackles the problem of finding graphs that maximize or locally maximize the algebraic connectivity in the space of graphs with a fixed number of vertices and edges. It is shown that some well-known classes of graphs such as star graphs, cycle graphs, complete bipartite graphs, and circulant graphs are algebraic connectivity maximizers or local maximizers under certain conditions. |
| doi_str_mv | 10.1109/TCNS.2015.2503561 |
| format | Article |
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| subjects | Algebraic connectivity consensus algorithm Convergence convergence rate Eigenvalues and eigenfunctions Laplace equations Laplacian matrix multiagent network Network topology Nickel Protocols Topology |
| title | Maximizing Algebraic Connectivity in the Space of Graphs With a Fixed Number of Vertices and Edges |
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