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Python third-party libraries (TPLs) are essential in modern software development, but upgrades often cause compatibility issues, leading to system failures. These issues fall into two categories: version compatibility issues (VCIs) and code…

Python is widely used in the open-source community, largely owing to the extensive support from diverse third-party libraries within the PyPI ecosystem. Nevertheless, the utilization of third-party libraries can potentially lead to…

Platforms like Stack Overflow and GitHub's gist system promote the sharing of ideas and programming techniques via the distribution of code snippets designed to illustrate particular tasks. Python, a popular and fast-growing programming…

We implement Ananke: an object-oriented Python package for causal inference with graphical models. At the top of our inheritance structure is an easily extensible Graph class that provides an interface to several broadly useful graph-based…

Knowledge graphs (KGs) have emerged as a prominent data representation and management paradigm. Being usually underpinned by a schema (e.g., an ontology), KGs capture not only factual information but also contextual knowledge. In some…

Graph domain adaptation has emerged as a promising approach to facilitate knowledge transfer across different domains. Recently, numerous models have been proposed to enhance their generalization capabilities in this field. However, there…

Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems. However, significant barriers exist in crossing disciplinary boundaries when most machine learning tools are developed in…

Call graphs play an important role in different contexts, such as profiling and vulnerability propagation analysis. Generating call graphs in an efficient manner can be a challenging task when it comes to high-level languages that are…

Pykg2vec is an open-source Python library for learning the representations of the entities and relations in knowledge graphs. Pykg2vec's flexible and modular software architecture currently implements 16 state-of-the-art knowledge graph…

Modern Python projects execute computational functions using native libraries and give Python interfaces to boost execution speed; hence, testing these libraries becomes critical to the project's robustness. One challenge is that existing…

Knowledge Graphs (KG) act as a great tool for holding distilled information from large natural language text corpora. The problem of natural language querying over knowledge graphs is essential for the human consumption of this information.…

Python applications depend on third-party native libraries that may be vendored within package distributions or installed on the host system. When vulnerabilities are discovered in these native libraries, determining which Python packages…

Knowledge Graphs (KGs) are increasingly adopted as a foundational technology for integrating heterogeneous data in domains such as climate science, cultural heritage, and the life sciences. Declarative mapping languages like R2RML and RML…

TorchKGE is a Python module for knowledge graph (KG) embedding relying solely on PyTorch. This package provides researchers and engineers with a clean and efficient API to design and test new models. It features a KG data structure, simple…

Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been…

Assessing developer proficiency in open-source software (OSS) projects is essential for understanding project dynamics, especially for expertise. This paper presents PyGress, a web-based tool designed to automatically evaluate and visualize…

Python has become a popular programming language because of its excellent programmability. Many modern software packages utilize Python for high-level algorithm design and depend on native libraries written in C/C++/Fortran for efficient…

Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities. NeuralKG-ind is the first…

In modern software development, Python third-party libraries play a critical role, especially in fields like deep learning and scientific computing. However, API parameters in these libraries often change during evolution, leading to…

There is an emerging trend of embedding knowledge graphs (KGs) in continuous vector spaces in order to use those for machine learning tasks. Recently, many knowledge graph embedding (KGE) models have been proposed that learn low dimensional…