This is a live page with my latest reading notes for various scientific topics.
Data Mining

ApFSM, A parallel algorithm for approximate frequent subgraph mining using Pregel

ManIACS, Approximate Mining of Frequent Subgraph Patterns through Sampling

Pangolin, An efficient and flexible graph mining system on CPU and GPU

Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs

SLOSH, Set LOcality Sensitive Hashing via Sliced Wasserstein Embeddings
Cheminformatics

Learning Local Equivariant Representations for LargeScale Atomistic Dynamics

Towards Effective and Generalizable FineTuning for Pretrained Molecular Graph Models

SelfSupervised Graph Transformer on LargeScale Molecular Data
Bioinformatics

Protein Structure Comparison by Alignment of Distance Matrices

Learning Structural Motif Representations for Efficient Protein Structure Search

Frequent subgraph mining for biologically meaningful structural motifs

Structurebased function prediction using graph convolution networks

Hydrogen bonds meet selfattention, all you need for generalpurpose protein structure embedding

Efficiently mining recurrent substructures from protein threedimensional structure graphs

ProteinNet, a standardized data set for machine learning of protein structure

Efficient networkguided multilocus association mapping with graph cuts