Katz centrality

In graph theory, the Katz centrality of a node is a measure of centrality in a network. It was introduced by Leo Katz in 1953 and is used to measure the relative degree of influence of an actor (or node) within a social network.^[1] Unlike typical centrality measures which consider only the shortest path (the geodesic) between a pair of actors, Katz centrality measures influence by taking into account the total number of walks between a pair of actors.^[2]

It is similar to Google's PageRank and to the eigenvector centrality.^[3]

Measurement

A simple social network: the nodes represent people or actors and the edges between nodes represent some relationship between actors

Katz centrality computes the relative influence of a node within a network by measuring the number of the immediate neighbors (first degree nodes) and also all other nodes in the network that connect to the node under consideration through these immediate neighbors. Connections made with distant neighbors are, however, penalized by an attenuation factor $\alpha$ .^[4] Each path or connection between a pair of nodes is assigned a weight determined by $\alpha$ and the distance between nodes as $\alpha ^{d}$ .

For example, in the figure on the right, assume that John's centrality is being measured and that $\alpha =0.5$ . The weight assigned to each link that connects John with his immediate neighbors Jane and Bob will be $(0.5)^{1}=0.5$ . Since Jose connects to John indirectly through Bob, the weight assigned to this connection (composed of two links) will be $(0.5)^{2}=0.25$ . Similarly, the weight assigned to the connection between Agneta and John through Aziz and Jane will be $(0.5)^{3}=0.125$ and the weight assigned to the connection between Agneta and John through Diego, Jose and Bob will be $(0.5)^{4}=0.0625$ .

Mathematical formulation

Let A be the adjacency matrix of a network under consideration. Elements $(a_{ij})$ of A are variables that take a value 1 if a node i is connected to node j and 0 otherwise. The powers of A indicate the presence (or absence) of links between two nodes through intermediaries. For instance, in matrix $A^{3}$ , if element $(a_{2,12})=1$ , it indicates that node 2 and node 12 are connected through some first and second degree neighbors of node 2. If $C_{\mathrm {Katz} }(i)$ denotes Katz centrality of a node i, then mathematically:

C_{\mathrm {Katz} }(i)=\sum _{k=1}^{\infty }\sum _{j=1}^{n}\alpha ^{k}(A^{k})_{ij}

Note that the above definition uses the fact that the element at location $(i,j)$ of $A^{k}$ reflects the total number of $k$ degree connections between nodes $i$ and $j$ . The value of the attenuation factor $\alpha$ has to be chosen such that it is smaller than the reciprocal of the absolute value of the largest eigenvalue of A.^[5] In this case the following expression can be used to calculate Katz centrality:

{\overrightarrow {C}}_{\mathrm {Katz} }=((I-\alpha A^{T})^{-1}-I){\overrightarrow {I}}

Here $I$ is the identity matrix, ${\overrightarrow {I}}$ is a vector of size n (n is the number of nodes) consisting of ones. $A^{T}$ denotes the transposed matrix of A and $(I-\alpha A^{T})^{-1}$ denotes matrix inversion of the term $(I-\alpha A^{T})$ .^[5]

An extension of this framework allows for the walks to be computed in a dynamical setting. ^[6]^[7] By taking a time dependent series of network adjacency snapshots of the transient edges, the dependency for walks to contribute towards a cumulative effect is presented. The arrow of time is preserved so that the contribution of activity is asymmetric in the direction of information propagation.

Network producing data of the form:

\left\{A^{[k]}\in \mathbb {R} ^{N\times N}\right\}\qquad {\text{for}}\quad k=0,1,2,\ldots ,M,

representing the adjacency matrix at each time $t_{k}$ . Hence,

\left(A^{[k]}\right)_{ij}={\begin{cases}1&{\text{there is an edge from node }}i{\text{ to node }}j{\text{ at time }}t_{k}\\0&{\text{otherwise}}\end{cases}}

The time points $t_{0}<t_{1}<\cdots <t_{M}$ are ordered but not necessarily equally spaced. $Q\in \mathbb {R} ^{N\times N}$ for which $(Q)_{ij}$ is a weighted count of the number of dynamic walks of length $w$ from node $i$ to node $j$ . The form for the dynamic communicability between participating nodes is:

{\mathcal {Q}}=\left(I-\alpha A^{[0]}\right)^{-1}\cdots \left(I-\alpha A^{[M]}\right)^{-1}.

This can be normalized via:

{\hat {\mathcal {Q}}}^{[k]}={\frac {{\hat {\mathcal {Q}}}^{[k-1]}\left(I-\alpha A^{[k]}\right)^{-1}}{\left\|{\hat {\mathcal {Q}}}^{[k-1]}\left(I-\alpha A^{[k]}\right)^{-1}\right\|}}.

Therefore centrality measures that quantify how effectively node $n$ can 'broadcast' and 'receive' dynamic messages across the network,

C_{n}^{\mathrm {broadcast} }:=\sum _{k=1}^{N}{\mathcal {Q}}_{nk}\quad \mathrm {and} \quad C_{n}^{\mathrm {receive} }:=\sum _{k=1}^{N}{\mathcal {Q}}_{kn}

.

Applications

Katz centrality can be used to compute centrality in directed networks such as citation networks and the World Wide Web.^[8]

Katz centrality is more suitable in the analysis of directed acyclic graphs where traditionally used measures like eigenvector centrality are rendered useless.^[8]

Katz centrality can also be used in estimating the relative status or influence of actors in a social network. The work presented in ^[9] shows the case study of applying a dynamic version of the Katz centrality to data from Twitter and focuses on particular brands which have stable discussion leaders. The application allows for a comparison of the methodology with that of human experts in the field and how the results are in agreement with a panel of social media experts.

In neuroscience, it is found that Katz centrality correlates with the relative firing rate of neurons in a neural network.^[10]. The temporal extension of the Katz centrality is applied to fMRI data obtained from a musical learning experiement in ^[11] where data is collected from the subjects before and after the learning process. The results show that the changes to the network structure over the musical exposure created in each session a quantification of the cross communicability that produced clusters in line with the success of learning.

References

↑ Katz, L. (1953). A New Status Index Derived from Sociometric Analysis. Psychometrika, 39–43.
↑ Hanneman, R. A., & Riddle, M. (2005). Introduction to Social Network Methods. Retrieved from http://faculty.ucr.edu/~hanneman/nettext/
↑ Vigna, S. (2016). Spectral ranking. Network Science. vol. 4, 4, 433-445. Retrieved from https://doi.org/10.1017/nws.2016.21
↑ Aggarwal, C. C. (2011). Social Network Data Analysis. New York, NY: Springer.
1 2 Junker, B. H., & Schreiber, F. (2008). Analysis of Biological Networks. Hoboken, NJ: John Wiley & Sons.
↑ Grindrod, Peter and Parsons, Mark C and Higham, Desmond J and Estrada, Ernesto (2011). "Communicability across evolving networks". Physical Review E. APS. 83 (4).
↑ Peter Grindrod and Desmond J. Higham. (2010). "Evolving graphs: Dynamical models, inverse problems and propagation". Proc. Roy. Soc. A. 466: 753–770.
1 2 Newman, M. E. (2010). Networks: An Introduction. New York, NY: Oxford University Press.
↑ Laflin, Peter and Mantzaris, Alexander V and Ainley, Fiona and Otley, Amanda and Grindrod, Peter and Higham, Desmond J (2013). "Discovering and validating influence in a dynamic online social network". Social Network Analysis and Mining. Springer. 3 (4).
↑ 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.
↑ Mantzaris, Alexander V., Danielle S. Bassett, Nicholas F. Wymbs, Ernesto Estrada, Mason A. Porter, Peter J. Mucha, Scott T. Grafton, and Desmond J. Higham. (2013). "Dynamic network centrality summarizes learning in the human brain". Journal of Complex Networks. 1 (1): 83–92.

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[katz1953-1] Katz, L. (1953). A New Status Index Derived from Sociometric Analysis. Psychometrika, 39–43.

[hanneman2005-2] Hanneman, R. A., & Riddle, M. (2005). Introduction to Social Network Methods. Retrieved from http://faculty.ucr.edu/~hanneman/nettext/

[vigna2016-3] Vigna, S. (2016). Spectral ranking. Network Science. vol. 4, 4, 433-445. Retrieved from https://doi.org/10.1017/nws.2016.21

[aggarwal2011-4] Aggarwal, C. C. (2011). Social Network Data Analysis. New York, NY: Springer.

[junker2008-5] 1 2 Junker, B. H., & Schreiber, F. (2008). Analysis of Biological Networks. Hoboken, NJ: John Wiley & Sons.

[grindrod2011communicability-6] Grindrod, Peter and Parsons, Mark C and Higham, Desmond J and Estrada, Ernesto (2011). "Communicability across evolving networks". Physical Review E. APS. 83 (4).

[grindrod2010Dynamical-7] Peter Grindrod and Desmond J. Higham. (2010). "Evolving graphs: Dynamical models, inverse problems and propagation". Proc. Roy. Soc. A. 466: 753–770.

[newman2010-8] 1 2 Newman, M. E. (2010). Networks: An Introduction. New York, NY: Oxford University Press.

[laflin2013discovering-9] Laflin, Peter and Mantzaris, Alexander V and Ainley, Fiona and Otley, Amanda and Grindrod, Peter and Higham, Desmond J (2013). "Discovering and validating influence in a dynamic online social network". Social Network Analysis and Mining. Springer. 3 (4).

[jmckay2017-10] 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.

[mantzaris2013fMRI-11] Mantzaris, Alexander V., Danielle S. Bassett, Nicholas F. Wymbs, Ernesto Estrada, Mason A. Porter, Peter J. Mucha, Scott T. Grafton, and Desmond J. Higham. (2013). "Dynamic network centrality summarizes learning in the human brain". Journal of Complex Networks. 1 (1): 83–92.