  Given a large graph with few node labels, how can we (a) identify whether
there is generalized network-effects (GNE) or not, (b) estimate GNE to explain
the interrelations among node classes, and (c) exploit GNE efficiently to
improve the performance on downstream tasks? The knowledge of GNE is valuable
for various tasks like node classification, and targeted advertising. However,
identifying GNE such as homophily, heterophily or their combination is
challenging in real-world graphs due to limited availability of node labels and
noisy edges. We propose NetEffect, a graph mining approach to address the above
issues, enjoying the following properties: (i) Principled: a statistical test
to determine the presence of GNE in a graph with few node labels; (ii) General
and Explainable: a closed-form solution to estimate the specific type of GNE
observed; and (iii) Accurate and Scalable: the integration of GNE for accurate
and fast node classification. Applied on real-world graphs, NetEffect discovers
the unexpected absence of GNE in numerous graphs, which were recognized to
exhibit heterophily. Further, we show that incorporating GNE is effective on
node classification. On a million-scale real-world graph, NetEffect achieves
over 7 times speedup (14 minutes vs. 2 hours) compared to most competitors.
