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Add directed hypergraph layers and Cora node classification example #15

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fee173f
Add configurable multi-head attention
shonalidixit Aug 9, 2026
deedcb1
Add directed hypergraph layers and Cora example
shonalidixit Aug 19, 2026
5a62a24
Complete Cora experiments and directed layer implementations
shonalidixit Aug 20, 2026
a99e0f0
Add initial glucose reaction preprocessing
shonalidixit Aug 30, 2026
e6b1495
Remove glucose-specific preprocessing examples
shonalidixit Sep 2, 2026
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3 changes: 2 additions & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -4,4 +4,5 @@
/Manifest.toml
/docs/Manifest.toml
/docs/build/
*.txt
*.txt
.DS_Store
15 changes: 14 additions & 1 deletion Project.toml
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@@ -1,9 +1,10 @@
name = "HyperGraphNeuralNetworks"
uuid = "1aebdcae-dc77-44ee-b348-f058d326ae63"
authors = ["Evan Walter Clark Spotte-Smith", "Punna Amornvivat"]
version = "0.0.1-DEV"
authors = ["Evan Walter Clark Spotte-Smith", "Punna Amornvivat"]

@espottesmith espottesmith Aug 19, 2026

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At this point, you should absolutely list yourself as an author.


[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
Expand All @@ -14,17 +15,29 @@ InvertedIndices = "41ab1584-1d38-5bbf-9106-f11c6c58b48f"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
MolecularGraph = "6c89ec66-9cd8-5372-9f91-fabc50dd27fd"
NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
RDKitMinimalLib = "44044271-7623-48dc-8250-42433c44e4b7"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Reactant = "3c362404-f566-11ee-1572-e11a4b42c853"
SimpleDirectedHypergraphs = "a42c6b43-640c-4761-b4a8-4f98c31d02e9"
SimpleHypergraphs = "aa4a32ff-dd5d-5357-90e3-e7a9512f0501"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[compat]
CSV = "0.10.17"
DataFrames = "1.8.2"
LinearAlgebra = ">= 1.10.0"
MolecularGraph = "0.22.0"
Optimisers = "0.4.9"
Plots = "1.41.6"
RDKitMinimalLib = "1.2.1"
Reactant = ">= 0.2.0"
SimpleDirectedHypergraphs = "0.1.0"
SimpleHypergraphs = "0.3.0"
StatsPlots = "0.15.8"
julia = ">= 1.10.0"
29 changes: 29 additions & 0 deletions data/cora/README
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This directory contains the a selection of the Cora dataset (www.research.whizbang.com/data).

The Cora dataset consists of Machine Learning papers. These papers are classified into one of the following seven classes:
Case_Based
Genetic_Algorithms
Neural_Networks
Probabilistic_Methods
Reinforcement_Learning
Rule_Learning
Theory

The papers were selected in a way such that in the final corpus every paper cites or is cited by atleast one other paper. There are 2708 papers in the whole corpus.

After stemming and removing stopwords we were left with a vocabulary of size 1433 unique words. All words with document frequency less than 10 were removed.


THE DIRECTORY CONTAINS TWO FILES:

The .content file contains descriptions of the papers in the following format:

<paper_id> <word_attributes>+ <class_label>

The first entry in each line contains the unique string ID of the paper followed by binary values indicating whether each word in the vocabulary is present (indicated by 1) or absent (indicated by 0) in the paper. Finally, the last entry in the line contains the class label of the paper.

The .cites file contains the citation graph of the corpus. Each line describes a link in the following format:

<ID of cited paper> <ID of citing paper>

Each line contains two paper IDs. The first entry is the ID of the paper being cited and the second ID stands for the paper which contains the citation. The direction of the link is from right to left. If a line is represented by "paper1 paper2" then the link is "paper2->paper1".
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