In graph theory, eigenvector centrality (also called eigencentrality) is a measure of the influence of a node in a network. Relative scores are assigned to all nodes in the network based on the concept that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes. A high eigenvector score means that a node is connected to many nodes who themselves have high scores.
Using the adjacency matrix to find eigenvector centrality
For a given graph with vertices let be the adjacency matrix, i.e. if vertex is linked to vertex , and otherwise. The relative centrality, , score of vertex can be defined as:
where is a set of the neighbors of and is a constant. With a small rearrangement this can be rewritten in vector notation as the eigenvector equation
In general, there will be many different eigenvalues for which a non-zero eigenvector solution exists. However, the additional requirement that all the entries in the eigenvector be non-negative implies (by the Perron–Frobenius theorem) that only the greatest eigenvalue results in the desired centrality measure. The component of the related eigenvector then gives the relative centrality score of the vertex in the network. The eigenvector is only defined up to a common factor, so only the ratios of the centralities of the vertices are well defined. To define an absolute score one must normalise the eigen vector e.g. such that the sum over all vertices is 1 or the total number of vertices n. Power iteration is one of many eigenvalue algorithms that may be used to find this dominant eigenvector. Furthermore, this can be generalized so that the entries in A can be real numbers representing connection strengths, as in a stochastic matrix.
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- Fletcher, Jack McKay and Wennekers, Thomas (2017). "From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity". International Journal of Neural Systems. 0 (0): 1750013. doi:10.1142/S0129065717500137.CS1 maint: Multiple names: authors list (link)