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Salvador Aguinaga's Projects

auralml icon auralml

Resources Limited Open Source Medical Tools

biblit icon biblit

Scientific literature Bibtex List

bluestsdk_android icon bluestsdk_android

Bluetooth low energy Sensors Technology Software Development Kit (Android version)

categorypaths icon categorypaths

Exploring human behavior: Modeling human navigation of information networks

cnrg icon cnrg

Code release for the paper "Modeling Graphs with Vertex Replacement Grammars" by Sikdar et al.

covid_xs icon covid_xs

Explorations into COVID and related infectious diseases

cv_resume icon cv_resume

Access a current copy of my CV and/or Resume

deepchem icon deepchem

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

fusuma icon fusuma

✍️Fusuma makes slides with Markdown easily.

grami icon grami

GraMi is a novel framework for frequent subgraph mining in a single large graph, GraMi outperforms existing techniques by 2 orders of magnitudes. GraMi supports finding frequent subgraphs as well as frequent patterns, Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Also, GraMi supports user-defined structural and semantic constraints over the results, as well as approximate results. For more details, check our paper: Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, and Panos Kalnis. GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph. PVLDB, 7(7):517-528, 2014.

graphlab icon graphlab

A framework for large-scale machine learning and graph computation.

hrg-nm icon hrg-nm

Hyperedge Replacement Grammars Network Model

hrg_nets icon hrg_nets

Exploring the limits of HRG for network modeling

inddgo icon inddgo

Integrated Network Decomposition & Dynamic programming for Graph Optimization problems

lightgbm icon lightgbm

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

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