It is well believed that object-object relations and object-scene relations inherently improve the accuracy of object detection. However, the way to efficiently model relations remains a problem. Graph Convolutional Network (GCN), an effective method to handle structured data with relations, inspires us to leverage graphs in modeling relations for objection detection tasks. In this work, we propose a novel approach, Graph Assisted Reasoning (GAR), to utilize a heterogeneous graph in modeling object-object relations and object-scene relations. GAR fuses the features from neigh-boring object nodes as well as scene nodes and produces better recognition than that produced from individual object nodes. Moreover, compared to previous approaches using Recurrent Neural Network (RNN), the light-weight and low-coupling architecture of GAR further facilitates its integration into the object detection module. Comprehensive experiments on PASCAL VOC and MS COCO datasets demonstrate the efficacy of GAR.