# Circle Feature Graphormer: Can Circle Features Stimulate Graph Transformer? Source: [Circle Feature Graphormer: Can Circle Features Stimulate Graph Transformer?]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:52:24.668Z Authors: Jingsong Lv, Hongyang Chen, Yao Qi, Lei Yu Published: 2023-09-11T03:58:26.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Link Prediction on ogbl-citation2 (val)](): Link Prediction; ogbl-citation2 (val); MRR: 89.87. - [Link Prediction on ogbl-citation2 (test)](): Link Prediction; ogbl-citation2 (test); MRR: 0.8997. ## Original sources and implementation [Original paper]() [PDF]()