  Automatic estimation of relative difficulty of a pair of questions is an
important and challenging problem in community question answering (CQA)
services. There are limited studies which addressed this problem. Past studies
mostly leveraged expertise of users answering the questions and barely
considered other properties of CQA services such as metadata of users and
posts, temporal information and textual content. In this paper, we propose
DiffQue, a novel system that maps this problem to a network-aided edge
directionality prediction problem. DiffQue starts by constructing a novel
network structure that captures different notions of difficulties among a pair
of questions. It then measures the relative difficulty of two questions by
predicting the direction of a (virtual) edge connecting these two questions in
the network. It leverages features extracted from the network structure,
metadata of users/posts and textual description of questions and answers.
Experiments on datasets obtained from two CQA sites (further divided into four
datasets) with human annotated ground-truth show that DiffQue outperforms four
state-of-the-art methods by a significant margin (28.77% higher F1 score and
28.72% higher AUC than the best baseline). As opposed to the other baselines,
(i) DiffQue appropriately responds to the training noise, (ii) DiffQue is
capable of adapting multiple domains (CQA datasets), and (iii) DiffQue can
efficiently handle 'cold start' problem which may arise due to the lack of
information for newly posted questions or newly arrived users.
