  This paper addresses the question of identifying the best candidate answer to
a question on Community Question Answer (CQA) forums. The problem is important
because Individuals often visit CQA forums to seek answers to nuanced
questions. We develop a novel induced relational graph convolutional network
(IR-GCN) framework to address the question. We make three contributions. First,
we introduce a modular framework that separates the construction of the graph
with the label selection mechanism. We use equivalence relations to induce a
graph comprising cliques and identify two label assignment mechanisms---label
contrast, label sharing. Then, we show how to encode these assignment
mechanisms in GCNs. Second, we show that encoding contrast creates
discriminative magnification---enhancing the separation between nodes in the
embedding space. Third, we show a surprising result---boosting techniques
improve learning over familiar stacking, fusion, or aggregation approaches for
neural architectures. We show strong results over the state-of-the-art neural
baselines in extensive experiments on 50 StackExchange communities.
