98 citations · 190 across the 7 of their papers we have counts for
7 papers
Supervised Hypergraph Reconstruction
Yanbang Wang, Jon Kleinberg
We study an issue commonly seen with graph data analysis: many real-world complex systems involving high-order interactions are best encoded by hypergraphs; however, their datasets…
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
Haoteng Yin, Muhan Zhang, Yanbang Wang +2
Subgraph-based graph representation learning (SGRL) has been recently proposed to deal with some fundamental challenges encountered by canonical graph neural networks (GNNs), and h…
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
Yanbang Wang, Yen-Yu Chang, Yunyu Liu +2
Temporal networks serve as abstractions of many real-world dynamic systems. These networks typically evolve according to certain laws, such as the law of triadic closure, which is…
Revisiting graph neural networks and distance encoding from a practical view
Haoteng Yin, Yanbang Wang, Pan Li
Graph neural networks (GNNs) are widely used in the applications based on graph structured data, such as node classification and link prediction. However, GNNs are often used as a…
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning
Pan Li, Yanbang Wang, Hongwei Wang +1
Learning representations of sets of nodes in a graph is crucial for applications ranging from node-role discovery to link prediction and molecule classification. Graph Neural Netwo…
Transfer Learning using Representation Learning in Massive Open Online Courses
Mucong Ding, Yanbang Wang, Erik Hemberg +1
In a Massive Open Online Course (MOOC), predictive models of student behavior can support multiple aspects of learning, including instructor feedback and timely intervention. Ongoi…
Using Detailed Access Trajectories for Learning Behavior Analysis
Yanbang Wang, Nancy Law, Erik Hemberg +1
Student learning activity in MOOCs can be viewed from multiple perspectives. We present a new organization of MOOC learner activity data at a resolution that is in between the fine…