Yao Ma

Dynamic Graph Neural Networks

2018 · arXiv (Cornell University)

Graphs, which describe pairwise relations between objects, are essential representations of many real-world data such as social networks. In recent years, graph neural networks, which extend the neural network models to graph data, have attracted …

Signed Graph Convolutional Network

2018 · arXiv (Cornell University)

Due to the fact much of today's data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. One recent direction that has shown fruitful results, and …

Node Similarity Preserving Graph Convolutional Networks

2020 · arXiv (Cornell University)

Graph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the graph structure and node features by aggregating and transforming information within …

Research on Effectiveness Evaluation of Multi-UAV System Based on Improved Information Entropy by Prior Data

2025 · Unmanned Systems

Due to the characteristics of multiple random factors and flexible equipment composition, the effectiveness of multi-Unmanned Aerial Vehicle (UAV) system is difficult to evaluate accurately. Based on the modification of prior calculation data, an improved …

Signed Graph Convolutional Networks

2018

Due to the fact much of today's data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. One recent direction that has shown fruitful results, and …

Graph Neural Networks for Social Recommendation

2019

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to …

Deep social collaborative filtering

2019

Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via their interactions based on collaborative filtering techniques. In addition to …