  Recurrent Neural Networks (RNNs) have been proven to be effective in modeling
sequential data and they have been applied to boost a variety of tasks such as
document classification, speech recognition and machine translation. Most of
existing RNN models have been designed for sequences assumed to be identically
and independently distributed (i.i.d). However, in many real-world
applications, sequences are naturally linked. For example, web documents are
connected by hyperlinks; and genes interact with each other. On the one hand,
linked sequences are inherently not i.i.d., which poses tremendous challenges
to existing RNN models. On the other hand, linked sequences offer link
information in addition to the sequential information, which enables
unprecedented opportunities to build advanced RNN models. In this paper, we
study the problem of RNN for linked sequences. In particular, we introduce a
principled approach to capture link information and propose a linked Recurrent
Neural Network (LinkedRNN), which models sequential and link information
coherently. We conduct experiments on real-world datasets from multiple domains
and the experimental results validate the effectiveness of the proposed
framework.
