Search Results for author:
Found 9 papers, 3 papers with code
Microstructures and Accuracy of Graph Recall by Large Language Models
Moreover, we find that more advanced LLMs have a striking dependence on the domain that a real-world graph comes from -- by yielding the best recall accuracy when the graph is narrated in a language style consistent with its original domain.
From Graphs to Hypergraphs: Hypergraph Projection and its Remediation
Such a modeling choice typically involves an underlying projection process that maps the original hypergraph onto a graph, and is common in graph-based analysis.
Supervised Hypergraph Reconstruction
To reconstruct hypergraph data, we start by analyzing hyperedge distributions in the projection, based on which we create a framework containing two modules: (1) to handle the enormous search space of potential hyperedges, we design a sampling strategy with efficacy guarantees that significantly narrows the space to a smaller set of candidates; (2) to identify hyperedges from the candidates, we further design a hyperedge classifier in two well-working variants that capture structural features in the projection.
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
Subgraph-based graph representation learning (SGRL) has been recently proposed to deal with some fundamental challenges encountered by canonical graph neural networks (GNNs), and has demonstrated advantages in many important data science applications such as link, relation and motif prediction.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
Temporal networks serve as abstractions of many real-world dynamic systems.
Revisiting graph neural networks and distance encoding from a practical view
We want to explain how DE makes GNNs fit for node classification and link prediction.
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning
DE captures the distance between the node set whose representation is to be learned and each node in the graph.
Using Detailed Access Trajectories for Learning Behavior Analysis
Student learning activity in MOOCs can be viewed from multiple perspectives.
Transfer Learning using Representation Learning in Massive Open Online Courses
It consists of two alternative transfer methods based on representation learning with auto-encoders: a passive approach using transductive principal component analysis and an active approach that uses a correlation alignment loss term.
