This review provides a comprehensive overview of the state-of-the-art methods of graph-
based networks from a deep learning perspective. Graph networks provide a generalized …

In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …

This work introduces DiGress, a discrete denoising diffusion model for generating graphs
with categorical node and edge attributes. Our model utilizes a discrete diffusion process …

Generating graph-structured data requires learning the underlying distribution of graphs.
Yet, this is a challenging problem, and the previous graph generative methods either fail to …

Pre-trained models for programming language have achieved dramatic empirical
improvements on a variety of code-related tasks such as code search, code completion …

Pre-trained models of code have achieved success in many important software engineering
tasks. However, these powerful models are vulnerable to adversarial attacks that slightly …

The ability to generate natural language sequences from source code snippets has a variety
of applications such as code summarization, documentation, and retrieval. Sequence-to …

The problem of fixing errors in programs has attracted substantial interest over the years.
The key challenge for building an effective code fixing tool is to capture a wide range of …

Code clones are semantically similar code fragments pairs that are syntactically similar or
different. Detection of code clones can help to reduce the cost of software maintenance and …

Graphs are ubiquitous in encoding relational information of real-world objects in many
domains. Graph generation, whose purpose is to generate new graphs from a distribution …