Graph generative models have been extensively studied in the data mining literature. While traditional techniques are based on generating structures that adhere to a pre-decided distribution, recent techniques have shifted towards learning this distribution directly from the data. While learning-based approaches have imparted significant improvement in quality, some limitations remain to be addressed. First, learning graph distributions introduces additional computational overhead, which limits their scalability to large graph databases. Second, many techniques only learn the structure and do not address the need to also learn node and edge labels, which encode important semantic information and influence the structure itself. Third, existing techniques often incorporate domain-specific rules and lack generalizability. Fourth, the experimentation of existing techniques is not comprehensive enough due to either using weak evaluation metrics or focusing primarily on synthetic or small datasets. In this work, we develop a domain-agnostic technique called GraphGen to overcome all of these limitations. GraphGen converts graphs to sequences using minimum DFS codes. Minimum DFS codes are canonical labels and capture the graph structure precisely along with the label information. The complex joint distributions between structure and semantic labels are learned through a novel LSTM architecture. Extensive experiments on million-sized, real graph datasets show GraphGen to be 4 times faster on average than state-of-the-art techniques while being significantly better in quality across a comprehensive set of 11 different metrics. Our code is released at https://github.com/idea-iitd/graphgen.
Protocol-agnostic and Data-free Backdoor Attacks on Pre-trained Models in RF Fingerprinting
Knight Foundation School of Computing and Information Sciences, Florida International University（Knight Foundation 计算与信息科学学院，佛罗里达国际大学）；Department of Electrical Engineering and Electronics, University of Liverpool（电子工程与电子系，利物浦大学）
Simulation-Based Pretraining and Domain Adaptation for Astronomical Time Series with Minimal Labeled Data
Massachusetts Institute of Technology, Cambridge, MA 02139, USA；Irvington High School, Fremont, CA 94538, USA；Center for Astrophysics, Harvard \& Smithsonian, Cambridge, MA 02138, USA
Model-agnostic Selective Labeling with Provable Statistical Guarantees
Department of Statistics and Data Science, Southern University of Science and Technology（统计与数据科学系，南方科技大学）；Department of Mathematics, The Chinese University of HongKong（数学系，香港中文大学）；School of Software Engineering, South China University of Technology（软件工程学院，华南理工大学）
DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypes
Department of Computing（计算系）；The Hong Kong Polytechnic University（香港理工大学）；Department of Computer Science（计算机科学系）；City University of Hong Kong（城市大学）；School of Software（软件学院）；Beihang University（北航）；University of Illinons at Chicago（伊利诺伊大学香槟分校）
Faculty of Information Technology and Communication Sciences, Tampere University（信息科技与通讯科学学院，坦佩雷大学）；Faculty of Medicine and the Department of Clinical Neurophysiology at the University of Helsinki and Helsinki University Hospital（医学学院及赫尔辛基大学临床神经生理学系，赫尔辛基大学医院）；Department of Electrical Engineering, Qatar University（电子工程系，卡塔尔大学）
