Yu Rong

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 …

On Self-Distilling Graph Neural Network

2020 · arXiv (Cornell University)

Recently, the teacher-student knowledge distillation framework has demonstrated its potential in training Graph Neural Networks (GNNs). However, due to the difficulty of training over-parameterized GNN models, one may not easily obtain a satisfactory teacher model …

Recognizing Predictive Substructures With Subgraph Information Bottleneck

2021 · IEEE Transactions on Pattern Analysis and Machine Intelligence

The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning. However, two disturbing factors, noise and redundancy in graph data, and lack of interpretation for prediction results, impede further development …

Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions

2023 · arXiv (Cornell University)

A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human …

Demonstration of a portable diffractive photon neural network system

2025

Diffractive photon neural network (DPNN) is a novel advanced calculation system, which utilizes optical hardward to implement convolution neural network calculations for different tasks. This talk demonstrates a portable diffractive DPNN system and its fundamental …

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 …