Title: GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks,
Abstract: In this paper, we generally formulate the dynamics prediction problem of
various network systems (e.g., the prediction of mobility, traffic and
topology) as the temporal link prediction task. Different from conventional
techniques of temporal link prediction that ignore the potential non-linear
characteristics and the informative link weights in the dynamic network, we
introduce a novel non-linear model GCN-GAN to tackle the challenging temporal
link prediction task of weighted dynamic networks. The proposed model leverages
the benefits of the graph convolutional network (GCN), long short-term memory
(LSTM) as well as the generative adversarial network (GAN). Thus, the dynamics,
topology structure and evolutionary patterns of weighted dynamic networks can
be fully exploited to improve the temporal link prediction performance.
Concretely, we first utilize GCN to explore the local topological
characteristics of each single snapshot and then employ LSTM to characterize
the evolving features of the dynamic networks. Moreover, GAN is used to enhance
the ability of the model to generate the next weighted network snapshot, which
can effectively tackle the sparsity and the wide-value-range problem of edge
weights in real-life dynamic networks. To verify the model's effectiveness, we
conduct extensive experiments on four datasets of different network systems and
application scenarios. The experimental results demonstrate that our model
achieves impressive results compared to the state-of-the-art competitors.