203 citations · 1k across the 101 of their papers we have counts for
18 papers · 1 filter
Independent Distribution Regularization for Private Graph Embedding
Qi Hu, Yangqiu Song
Learning graph embeddings is a crucial task in graph mining tasks. An effective graph embedding model can learn low-dimensional representations from graph-structured data for data…
Logical Message Passing Networks with One-hop Inference on Atomic Formulas
Zihao Wang, Yangqiu Song, Ginny Y. Wong +1
Complex Query Answering (CQA) over Knowledge Graphs (KGs) has attracted a lot of attention to potentially support many applications. Given that KGs are usually incomplete, neural m…
Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform
Huiru Xiao, Xin Liu, Yangqiu Song +2
The choice of geometric space for knowledge graph (KG) embeddings can have significant effects on the performance of KG completion tasks. The hyperbolic geometry has been shown to…
Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies
Yinghua Zhang, Yangqiu Song, Kun Bai +1
Fine-tuning can be vulnerable to adversarial attacks. Existing works about black-box attacks on fine-tuned models (BAFT) are limited by strong assumptions. To fill the gap, we prop…
Boosting Graph Structure Learning with Dummy Nodes
Xin Liu, Jiayang Cheng, Yangqiu Song +1
With the development of graph kernels and graph representation learning, many superior methods have been proposed to handle scalability and oversmoothing issues on graph structure…
Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching
Xin Liu, Yangqiu Song
Graph neural networks (GNNs) and message passing neural networks (MPNNs) have been proven to be expressive for subgraph structures in many applications. Some applications in hetero…
Differentially Private Federated Knowledge Graphs Embedding
Hao Peng, Haoran Li, Yangqiu Song +2
Knowledge graph embedding plays an important role in knowledge representation, reasoning, and data mining applications. However, for multiple cross-domain knowledge graphs, state-o…
Unit Ball Model for Embedding Hierarchical Structures in the Complex Hyperbolic Space
Huiru Xiao, Caigao Jiang, Yangqiu Song +2
Learning the representation of data with hierarchical structures in the hyperbolic space attracts increasing attention in recent years. Due to the constant negative curvature, the…
Wasserstein Graph Neural Networks for Graphs with Missing Attributes
Zhixian Chen, Tengfei Ma, Yangqiu Song +1
Missing node attributes is a common problem in real-world graphs. Graph neural networks have been demonstrated power in graph representation learning while their performance is aff…
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
Zhixian Chen, Tengfei Ma, Zhihua Jin +2
Graph convolutional networks (GCNs) have achieved great success on graph-structured data. Many graph convolutional networks can be thought of as low-pass filters for graph signals.…
Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning
Yinghua Zhang, Yangqiu Song, Jian Liang +2
Transfer learning has become a common practice for training deep learning models with limited labeled data in a target domain. On the other hand, deep models are vulnerable to adve…
Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network
Haotian Li, Huan Wei, Yong Wang +2
Student performance prediction is critical to online education. It can benefit many downstream tasks on online learning platforms, such as estimating dropout rates, facilitating st…
Fisher Deep Domain Adaptation
Yinghua Zhang, Yu Zhang, Ying Wei +3
Deep domain adaptation models learn a neural network in an unlabeled target domain by leveraging the knowledge from a labeled source domain. This can be achieved by learning a doma…
Neural Subgraph Isomorphism Counting
Xin Liu, Haojie Pan, Mutian He +3
In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Different from other traditional graph learning problems such as node classification…
RWNE: A Scalable Random-Walk-Based Network Embedding Framework with Personalized Higher-Order Proximity Preserved
Jianxin Li, Cheng Ji, Hao Peng +4
Higher-order proximity preserved network embedding has attracted increasing attention. In particular, due to the superior scalability, random-walk-based network embedding has also…
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding
Yu He, Yangqiu Song, Jianxin Li +3
Heterogeneous information network (HIN) embedding has gained increasing interests recently. However, the current way of random-walk based HIN embedding methods have paid few attent…
Efficient Path Prediction for Semi-Supervised and Weakly Supervised Hierarchical Text Classification
Huiru Xiao, Xin Liu, Yangqiu Song
Hierarchical text classification has many real-world applications. However, labeling a large number of documents is costly. In practice, we can use semi-supervised learning or weak…
World Knowledge as Indirect Supervision for Document Clustering
Chenguang Wang, Yangqiu Song, Dan Roth +2
One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consid…