Search Results for author:
Found 8 papers, 5 papers with code
TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions
While many tensor compression algorithms are available, many of them rely on strong data assumptions regarding its order, sparsity, rank, and smoothness.
Learning Disentangled Representations in Signed Directed Graphs without Social Assumptions
In this paper, we propose DINES, a novel method for learning disentangled node representations in signed directed graphs without social assumptions.
NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors
The updates take time linear in the number of non-zeros in the input matrix, and the approximation of each entry can be retrieved in logarithmic time.
Time-aware Random Walk Diffusion to Improve Dynamic Graph Learning
How can we augment a dynamic graph for improving the performance of dynamic graph neural networks?
Accurate Node Feature Estimation with Structured Variational Graph Autoencoder
Given a graph with partial observations of node features, how can we estimate the missing features accurately?
Signed Graph Diffusion Network
In this paper, we propose Signed Graph Diffusion Network (SGDNet), a novel graph neural network that achieves end-to-end node representation learning for link sign prediction in signed social graphs.
T-GAP: Learning to Walk across Time for Temporal Knowledge Graph Completion
However, most of the existing mod-els for TKG completion extend static KG embeddings that donot fully exploit TKG structure, thus lacking in 1) account-ing for temporally relevant events already residing in the lo-cal neighborhood of a query, and 2) path-based inference that facilitates multi-hop reasoning and better interpretability.
Fast and Accurate Pseudoinverse with Sparse Matrix Reordering and Incremental Approach
How can we compute the pseudoinverse of a sparse feature matrix efficiently and accurately for solving optimization problems?
