  While node semantics have been extensively explored in social networks,
little research attention has been paid to profile edge semantics, i.e., social
relations. Ideal edge semantics should not only show that two users are
connected, but also why they know each other and what they share in common.
However, relations in social networks are often hard to profile, due to noisy
multi-modal signals and limited user-generated ground-truth labels.
  In this work, we aim to develop a unified and principled framework that can
profile user relations as edge semantics in social networks by integrating
multi-modal signals in the presence of noisy and incomplete data. Our framework
is also flexible towards limited or missing supervision. Specifically, we
assume a latent distribution of multiple relations underlying each user link,
and learn them with multi-modal graph edge variational autoencoders. We encode
the network data with a graph convolutional network, and decode arbitrary
signals with multiple reconstruction networks. Extensive experiments and case
studies on two public DBLP author networks and two internal LinkedIn member
networks demonstrate the superior effectiveness and efficiency of our proposed
model.
