  Social networks involve both positive and negative relationships, which can
be captured in signed graphs. The {\em edge sign prediction problem} aims to
predict whether an interaction between a pair of nodes will be positive or
negative. We provide theoretical results for this problem that motivate natural
improvements to recent heuristics.
  The edge sign prediction problem is related to correlation clustering; a
positive relationship means being in the same cluster. We consider the
following model for two clusters: we are allowed to query any pair of nodes
whether they belong to the same cluster or not, but the answer to the query is
corrupted with some probability $0<q<\frac{1}{2}$. Let $\delta=1-2q$ be the
bias. We provide an algorithm that recovers all signs correctly with high
probability in the presence of noise with $O(\frac{n\log
n}{\delta^2}+\frac{\log^2 n}{\delta^6})$ queries. This is the best known result
for this problem for all but tiny $\delta$, improving on the recent work of
Mazumdar and Saha \cite{mazumdar2017clustering}. We also provide an algorithm
that performs $O(\frac{n\log n}{\delta^4})$ queries, and uses breadth first
search as its main algorithmic primitive. While both the running time and the
number of queries for this algorithm are sub-optimal, our result relies on
novel theoretical techniques, and naturally suggests the use of edge-disjoint
paths as a feature for predicting signs in online social networks.
Correspondingly, we experiment with using edge disjoint $s-t$ paths of short
length as a feature for predicting the sign of edge $(s,t)$ in real-world
signed networks. Empirical findings suggest that the use of such paths improves
the classification accuracy, especially for pairs of nodes with no common
neighbors.
