(here $D_i^{(c)}$ corresponds to the edge-count that includes edges with exactly $i$ anomalous vertices). The reader can verify that $\frac{Var_1(T^*)}{Var_0(T^*)} \to 1$. Moreover, given that the limiting distribution (under the null) is normal, we write
(i.e. there is signal in the null-to-anomaly connectivity) then the limiting power $\beta_2^W > \alpha$ when $\frac{m(n-m)}{\sqrt{n^3}} \to 0$ or, equivalently, when $m = \Omega(\sqrt{n})$ (similarly, if $m = \omega(\sqrt{n})$ then $\beta_2^W \to 1$). Furthermore, if
c∑wcD1(c)=0
(i.e. there is no signal in the null-to-anomaly connectivity) the limiting power $\beta_2^W > \alpha$ when $\sum_c w_c D_2^{(c)} \neq 0$ and $\frac{m^2}{\sqrt{n^3}} \to 0$ (which is equivalent to $m = \Omega(\sqrt[4]{n^3})$). Moreover, if $m = \omega(\sqrt[4]{n^3})$ under these conditions then $\beta_2^W \to 1$.
It follows that the optimal choice of weights ($w_1, \dots, w_K$) is the one which maximizes the expression
n→∞lim(Var0(T∗)E1[T2W]−E0[T2W])
in either of the two above-mentioned cases.
3.2 The Attributed Number of Triangles Fusion
We begin by writing
τ=c∈[K]∑τc+b=c∑τb,c+d=b,c∑τb,c,d.
We denote the number-of-triangles fusion invariant to be