Self-Contrastive Graph Diffusion Network
Yixian Ma Kun Zhan
Augmentation techniques and sampling strategies are crucial in contrastive
learning, but in most existing works, augmentation techniques require careful
design, and their sampling strategies can only capture a small amount of
intrinsic supervision information. Additionally, the existing methods require
complex designs to obtain two different representations of the data. To
overcome these limitations, we propose a novel framework called the
Self-Contrastive Graph Diffusion Network (SCGDN). Our framework consists of two
main components: the Attentional Module (AttM) and the Diffusion Module (DiFM).
AttM aggregates higher-order structure and feature information to get an
excellent embedding, while DiFM balances the state of each node in the graph
through Laplacian diffusion learning and allows the cooperative evolution of
adjacency and feature information in the graph. Unlike existing methodologies,
SCGDN is an augmentation-free approach that avoids "sampling bias" and semantic
drift, without the need for pre-training. We conduct a high-quality sampling of
samples based on structure and feature information. If two nodes are neighbors,
they are considered positive samples of each other. If two disconnected nodes
are also unrelated on $k$NN graph, they are considered negative samples for
each other. The contrastive objective reasonably uses our proposed sampling
strategies, and the redundancy reduction term minimizes redundant information
in the embedding and can well retain more discriminative information. In this
novel framework, the graph self-contrastive learning paradigm gives expression
to a powerful force. SCGDN effectively balances between preserving high-order
structure information and avoiding overfitting. The results manifest that SCGDN
can consistently generate outperformance over both the contrastive methods and
the classical methods.