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Found 6 papers, 5 papers with code
Topological Node2vec: Enhanced Graph Embedding via Persistent Homology
Node2vec is a graph embedding method that learns a vector representation for each node of a weighted graph while seeking to preserve relative proximity and global structure.
Matrix Method for Persistence Modules on Commutative Ladders of Finite Type
In this work, we view a persistence module $M$ on $CL_n(\tau)$ as a morphism between zigzag modules, which can be expressed in a block matrix form.
Representation Theory Algebraic Topology
Persistence Diagrams with Linear Machine Learning Models
Persistence diagrams have been widely recognized as a compact descriptor for characterizing multiscale topological features in data.
Kernel method for persistence diagrams via kernel embedding and weight factor
Topological data analysis is an emerging mathematical concept for characterizing shapes in multi-scale data.
Ranked #4 on
Graph Classification
on NEURON-MULTI
Persistence weighted Gaussian kernel for topological data analysis
Topological data analysis (TDA) is an emerging mathematical concept for characterizing shapes in complex data.
Algebraic Topology
