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

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Data Mining

• Ap-FSM, A parallel algorithm for approximate frequent subgraph mining using Pregel

• ASAP, Fast Approximate Graph Pattern Mining at Scale

• Frequent pattern mining in big social graphs

• Extension of Canonical Adjacency Matrices for Frequent Approximate Subgraph Mining on Multi-Graph Collections

• G-Finder, Approximate Attributed Subgraph matching

• gSpan, graph-based substructure pattern mining

• Approximate Graph Mining with Label Costs

• Large graph sampling algorithm for frequent subgraph mining

• ManIACS, Approximate Mining of Frequent Subgraph Patterns through Sampling

• MARGIN, Maximal Frequent Subgraph Mining

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

• RAM, Randomized Approximate Graph Mining

• REAFUM, Representative Approximate Frequent Subgraph Mining

• Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs

• SLOSH, Set LOcality Sensitive Hashing via Sliced Wasserstein Embeddings

• Representation Learning for Frequent Subgraph Mining

• An Efficient System for Subgraph Discovery

• Motifs in temporal networks

• TKG, Efficient Mining of Top-K Frequent Subgraphs

Cheminformatics

• Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics

• Towards Effective and Generalizable Fine-Tuning for Pre-trained Molecular Graph Models

• Self-Supervised Graph Transformer on Large-Scale Molecular Data

Bioinformatics

• Assessment of ligand binding site predictions in CASP10

• Protein Structure Comparison by Alignment of Distance Matrices

• Learning Structural Motif Representations for Efficient Protein Structure Search

• Foldseek fast and accurate protein structure search

• Frequent subgraph mining for biologically meaningful structural motifs

• Structure-based function prediction using graph convolution networks

• Hydrogen bonds meet self-attention, all you need for general-purpose protein structure embedding

• P2Rank, Machine Learning Based Tool for Rapid and Accurate Prediction of Ligand Binding Sites from Protein Structure

• Efficiently mining recurrent substructures from protein three-dimensional structure graphs

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

• Interrogating RNA-small molecule interactions with structure probing and AI augmented molecular simulations.

• Efficient network-guided multi-locus association mapping with graph cuts

Theoretical Biology

• Populations of genetic circuits are unable to find the fittest solution in a multilevel genotype-phenotype map

Machine Learning

• Explanatory Learning, Beyond Empricism in Neural Networks

• Motif-Driven Contrastive Learning of Graph Representations

• Molecular Graph Representation Learning via Heterogeneous Motif Graph Construction

• Prototypical Networks for Few-shot Learning

Graph Machine Learning

• Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

• Motif-Aware Adversarial Graph Representation Learning

• Motif Prediction with Graph Neural Networks

• Subgraph Augmentation with Applicaiton to Graph Mining

• Sampling Subgraph Network With Application to Graph Classification