CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation

• DOI: 10.1145/3487553.3524721
• Publication Date: 2022-08-16T22:41:30Z
• Authors: Yassaman Ommi; Matin Yousefabadi; Faezeh Faez; Amirmojtaba Sabour; Mahdieh Soleymani...; Hamid R. Rabiee
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 01 natural sciences; Machine Learning (cs.LG); 0105 earth and related environmental sciences

Graph data structures are fundamental for studying connected entities. With an increase in the number of applications where data is represented as graphs, the problem of graph generation has recently become a hot topic. However, despite its significance, conditional graph generation that creates graphs with desired features is relatively less explored in previous studies. This paper addresses the problem of class-conditional graph generation that uses class labels as generation constraints by introducing the Class Conditioned Graph Generator (CCGG). We built CCGG by injecting the class information as an additional input into a graph generator model and including a classification loss in its total loss along with a gradient passing trick. Our experiments show that CCGG outperforms existing conditional graph generation methods on various datasets. It also manages to maintain the quality of the generated graphs in terms of distribution-based evaluation metrics.