Circle Feature Graphormer: Can Circle Features Stimulate Graph
  Transformer?
Jingsong Lv Hongyang Chen Yao Qi Lei Yu
In this paper, we introduce two local graph features for missing link
prediction tasks on ogbl-citation2. We define the features as Circle Features,
which are borrowed from the concept of circle of friends. We propose the
detailed computing formulas for the above features. Firstly, we define the
first circle feature as modified swing for common graph, which comes from
bipartite graph. Secondly, we define the second circle feature as bridge, which
indicates the importance of two nodes for different circle of friends. In
addition, we firstly propose the above features as bias to enhance graph
transformer neural network, such that graph self-attention mechanism can be
improved. We implement a Circled Feature aware Graph transformer (CFG) model
based on SIEG network, which utilizes a double tower structure to capture both
global and local structure features. Experimental results show that CFG
achieves the state-of-the-art performance on dataset ogbl-citation2.