Wenbing Huang

Unsupervised Adversarial Graph Alignment with Graph Embedding

2019 · arXiv (Cornell University)

Graph alignment, also known as network alignment, is a fundamental task in social network analysis. Many recent works have relied on partially labeled cross-graph node correspondences, i.e., anchor links. However, due to the privacy and …

A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful …

YuLan: An Open-source Large Language Model

2024 · arXiv (Cornell University)

Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many open-source LLMs have been released with technical reports, the lack of training …

Large Language-Geometry Model: When LLM meets Equivariance

2025 · arXiv (Cornell University)

Accurately predicting 3D structures and dynamics of physical systems is crucial in scientific applications. Existing approaches that rely on geometric Graph Neural Networks (GNNs) effectively enforce $\mathrm{E}(3)$-equivariance, but they often fall in leveraging extensive broader …

Adaptive Sampling Towards Fast Graph Representation Learning

2018 · arXiv (Cornell University)

Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices. The main challenge of adapting GCNs on large-scale graphs is the scalability issue that it incurs heavy cost both in …

Graph Representation Learning via Graphical Mutual Information Maximization

2020

The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision. This paper investigates how to preserve and …