cograph (version 2.7.2)

centrality_shapley_game1: Shapley Value Centrality (Games 1, 2 and 3)

Description

Game-theoretic centrality of Michalak, Aadithya, Szczepanski, Ravindran and Jennings (2013): the Shapley value of each node in a coalition game whose worth \(v(C)\) is the number of nodes a coalition \(C\) "covers". Each game has a closed form, so the values are exact and cost linear time.

Usage

centrality_shapley_game1(x, ...)
centrality_shapley_game2(x, shapley_k = 2, ...)
centrality_shapley_game3(x, shapley_cutoff = 2, ...)

Value

Named numeric vector, one Shapley value per node.

Arguments

x

Network input (matrix, igraph, network, cograph_network, tna object).

...

Additional arguments passed to centrality.

shapley_k

Neighbor threshold \(k\) for game 2. Default 2.

shapley_cutoff

Hop cutoff for game 3. Default 2.

Details

Game 1 (shapley_game1)

\(v(C)\) = nodes in \(C\) or adjacent to it. \(SV(v) = \sum_{u \in \{v\} \cup N(v)} 1 / (1 + k_u)\).

Game 2 (shapley_game2)

\(v(C)\) = nodes in \(C\) or with at least \(k\) neighbors in \(C\). \(SV(v) = \min(1, k / (1 + k_v)) + \sum_{u \in N(v)} \max(0, (k_u - k + 1) / (k_u (1 + k_u)))\). With \(k = 1\) this is game 1. Threshold via shapley_k (default 2).

Game 3 (shapley_game3)

\(v(C)\) = nodes within shapley_cutoff hops of \(C\) (default 2). \(SV(v) = \sum_{u \in \{v\} \cup N_d(v)} 1 / (1 + |N_d(u)|)\), where \(N_d(u)\) is the set of nodes within \(d\) hops of \(u\). With cutoff 1 this is game 1.

Values in every game sum to the number of nodes (efficiency). Higher values mark nodes whose presence adds more coverage to a typical coalition. Degrees exclude self-loops, as in the paper. On a directed graph the coverage runs along out-edges and the denominators use in-degrees (the paper's stated extension); distances for game 3 are hop counts, so edge weights are ignored.

Validated against exact Shapley values obtained by enumerating every coalition on random graphs of up to eight nodes, including graphs with isolates, self-loops and several components.

References

Michalak, T. P., Aadithya, K. V., Szczepanski, P. L., Ravindran, B., & Jennings, N. R. (2013). Efficient computation of the Shapley value for game-theoretic network centrality. Journal of Artificial Intelligence Research, 46, 607-650.

See Also

centrality for computing multiple measures at once.

Examples

star5 <- matrix(0, 5, 5)
star5[1, 2:5] <- 1; star5[2:5, 1] <- 1
rownames(star5) <- colnames(star5) <- LETTERS[1:5]
centrality_shapley_game1(star5)
centrality_shapley_game2(star5, shapley_k = 2)
centrality_shapley_game3(star5, shapley_cutoff = 1)