Temporal Graph Representation Learning with Adaptive Augmentation
  Contrastive
Hongjiang Chen Pengfei Jiao Huijun Tang Huaming Wu
Temporal graph representation learning aims to generate low-dimensional
dynamic node embeddings to capture temporal information as well as structural
and property information. Current representation learning methods for temporal
networks often focus on capturing fine-grained information, which may lead to
the model capturing random noise instead of essential semantic information.
While graph contrastive learning has shown promise in dealing with noise, it
only applies to static graphs or snapshots and may not be suitable for handling
time-dependent noise. To alleviate the above challenge, we propose a novel
Temporal Graph representation learning with Adaptive augmentation Contrastive
(TGAC) model. The adaptive augmentation on the temporal graph is made by
combining prior knowledge with temporal information, and the contrastive
objective function is constructed by defining the augmented inter-view contrast
and intra-view contrast. To complement TGAC, we propose three adaptive
augmentation strategies that modify topological features to reduce noise from
the network. Our extensive experiments on various real networks demonstrate
that the proposed model outperforms other temporal graph representation
learning methods.