Most of the time, successful adjacency formulas are "built around specific and deep insights about customer behavior," he writes.
Together, Friend states define the time-varying adjacency matrix (fancyscript{A} k)) of the directed and weighted graph (fancyscript{G}(k) = {fancyscript{V},fancyscript{E}^k}) describing the network at time step k, where (fancyscript{V} := N) is the set of nodes and (fancyscript{E}^k = bigcup _{i in N}FS^k_{i}) is the set of weighted edges recorded by nodes' Friend states.
Suppose we have an (n times n) adjacency matrix with z nonzeros as an input to rank-2 SymNMF.
If A is the (n times n) adjacency matrix for a graph, such that A[i, j] = 1 if there is an edge connecting (v_{i}) to (v_{j}) (for undirected graphs) and A[i, j] = 0 if there is no edge connecting (v_{i}) and (v_{j}).
This instantaneous contact graph is represented as a time-dependent adjacency matrix Atij, such that Atij = 1 if the RFID tags i and j exchanged at least one packet at the lowest radio power during the time inteval [t-Δt, t], and Atij = 0 otherwise.
The weighted network of N = 1354 drugs, C, has (N times N) weighted adjacency matrix.
The input to the algorithm is an (n times n) Boolean adjacency matrix (M) of (D), that is, the entry (M_{i,j}) is 1 if there is a directed edge from vertex (i) to vertex (j) and 0 otherwise.
S If A i and A j are activity profiles of drugs i,j, that is the proteins k for which A i(k) =1 and A j(k) =1, then (S_{ij} = left| {A_{ileft( k right)} cap A_{jleft( k right)} } right|.) Here S has (N times N) weighted adjacency matrix.
end{aligned} (1 where (sigma _i k)=max left{ left| mathcal {N}_i k)right|,1right} ), (mathcal {N}_i k):=lbrace j in mathcal {V} mid a_{ji} k)=1 rbrace ) being the in-neighbors set of i, with (a_{ji} k)) being the jith element of the time-varying binary adjacency matrix A(k) associated to the interaction graph (mathcal {G} k)).
