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Hello,
I am trying to manually add edges to a hypergraph 'H'. First, I tried H.edges.add(), but it didn't show it in the edges list. I then read the documentation and in entity.py it says to use Hypergraph.add_edge or Hypergraph.add_node_to_edge instead, but none of the two is defined in the Hypergraph class, so they are not callable by H. Am I missing something?

Nitpick: The degree_dist() method has a name that is perhaps not the best fit. What this function returns is not a distribution, but a list of degrees. In graph theory this is called a degree sequence, not a degree distribution. What is normally understood as a degree distribution is the fraction of times each degree appears in the degree sequence.

Ref openjournals/joss-reviews#6016

Describe the bug

LICENSE is provided in LICENSE.rst but it not clearly OSI compliant.

Additional context

I am reviewing this package for JOSS openjournals/joss-reviews#6016 (comment)

It looks like the license is intended to be the BSD 3-clause license (at OSI here: https://opensource.org/license/bsd-3-clause/ ) but:

• there is a typo at the end of the 2nd clause which currently reads "and/or other" but should read "and/or other materials provided with the distribution."
• the third clause has been edited in a way as to make it unclear if it still matches the BSD 3-clause license. In particular "Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software" has been changed to "neither the name Battelle Memorial Institute or Battelle may be used in any form whatsoever without the express written consent of Battelle.".

The modification to clause 3 appears to be a substantial reduction in the grant.

I suggest that you either:

• use the BSD 3-clause license directly; or,
• provide enough material to reviewers to assure us that this license is in fact "the contents of an OSI approved software license"

I tried to generate a hypergraph using node neighborhood information from a matrix K of size |N| x |N|. Using the simple graph edges list, if the length (distance) of two given nodes is less than or equal to k, then I put 1 in the K matrix, 0 otherwise. 1 denotes that the two given nodes are neighbors, and 0 denotes that nodes are not neighbors. After generating the K matrix, I used it to represent hyperedges where each row of K represents the neighborhood of a particular node. (a hyperedge).

The problem is, after generating a hypergraph, it shows the correct number of hyperedges using the H.edges method, however, it gives the wrong number of nodes using the H.nodes method. The total nodes are very less than the total nodes given by the H.nodes method,

Also, I tried the example graph given in the documentation of the library. It has 13 nodes and 8 hyperedges. H.nodes and H.edges methods show the correct number of nodes and edges for this graph.

Can't I use the defined K matrix for hypergraph generation?

Describe the bug

bursh up should be brush up (funny tho).

Near the bottom the link to CODE_OF_CONDUCT is broken: it needs a .md extension.

I made the following diagram using Hypernetworkx's draw command.

The node fst_score node appears really close to the 233 hyperedge, making it hard to distinguish whether the node is a member of the hyperedge. Is there a setting an argument somewhere that would allow me to ensure that the hyperedges maintain a minimal distance from the nodes?

Once I build the hypergraph is it possible to get a list of Automorphisms in the hypergraph?

Hi,
Maybe I am just blind (and/or stupid), but how can I identify (easily) all hyperedges of a given node n in a (static) hypergraph H in HyperNetX?

The solution I came up is:

[edge for edge, nodes in H.incidence_dict.items() if n in nodes]

But probably you had another, more performant solution in mind?
Thank you!

For pip install hypernetx, the packages igraph and celluloid are not installed by default. One has to do it manually. This is also the reason why the tutorial fails (#73).
What about adding them to setup.py?

I tried the programs in the tutorial and also the code of the guy that reported the other issue and i get the following error:

/usr/local/lib/python3.7/site-packages/hypernetx/drawing/rubber_band.py:97: FutureWarning: arrays to stack must be passed as a "sequence" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.

I don't know if the problem is that I use python3

Is your feature request related to a problem? Please describe.

As per Tutorial 1, calling edge_distance (H.edge_distance(4, 6, s=2)) can generate a warning.

A mild improvement would be to make the warning a specific class in hypernetx (as the Exceptions are); this gives finer control over warnings to users.

(i do not require this to be fixed for my JOSS review)

Some documentation pages contain $-delimited LaTeX notation that is not rendered.

Example:

https://hypernetx.readthedocs.io/en/latest/modularity.html

Ref: openjournals/joss-reviews#6016

Describe the bug

The hyperlink https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-020-00231-0%22 appears in Tutorial 1 in the Connected Components section, but the link is broken (thanks Springer).

I passed a non-empty list containing the edge numbers to remove_edges(), all of which are present in the hypergraph, but it does not remove any edges in the hypergraph.

This is my code:

list_to_remove = [1, 2, 3]
HG_removed = HG.remove_edges(list_to_remove)

but when I check:
print(len(HG.edges())
and
print(len(HG_removed.edges())

they are both still at 1000.

Thanks in advance for the help!

Should I use edge_weight_prop,and node_weight_prop to assign values to edge weights and node weights? I noticed that there is a cell_weigh property, I want to know what cell weight represents

Hello,
I've recently started to use hypernetx and I found the following error if I try to access the dataframe associated with a Hypergraph. I simply created an instance 'H' of the Hypergraph class and added a multiedge. When I try to access it through 'H.dataframe', the python interpreter returns:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/homebrew/Caskroom/miniforge/base/lib/python3.10/site-packages/hypernetx/classes/hypergraph.py", line 588, in dataframe
    return self._dataframe
AttributeError: 'Hypergraph' object has no attribute '_dataframe'. Did you mean: 'dataframe'?

I have installed the last version of hypernetx available (hypernetx-2.0.0.post1) for python 3.10.8

Dear Author:

class SixByFive():
    """Example hypergraph with 6 nodes and 5 edges"""
    def __init__(self):
        mat = np.array([[1, 1, 1, 0, 0, 0], [1, 0, 1, 0, 1, 0], [1, 1, 0, 0, 1, 1], [0, 1, 1, 1, 0, 0], [1, 1, 1, 1, 0, 0]]).transpose()
        self.hypergraph = hnx.Hypergraph.from_numpy_array(mat)

H = SixByFive().hypergraph

def get_s_path(g, s, source):
    lg = g.get_linegraph(s=s, edges=True)
    print(list(lg.edges))
    try:
        path_dist = nx.single_source_shortest_path(lg, source)
    except (nx.NetworkXNoPath, nx.NodeNotFound):
        warnings.warn(f"No {s}-path between {source} and 1")
        edge_dist = np.inf

    return path_dist
path = get_s_path(H, s=3, source='e0')
print(path)
```python
![Snipaste_2023-11-11_03-41-45](https://github.com/pnnl/HyperNetX/assets/51594106/a76eeda7-9a50-4a38-aaff-8e934f13d62f)
![Snipaste_2023-11-11_03-33-53](https://github.com/pnnl/HyperNetX/assets/51594106/4493ddfd-1078-4708-acff-c54dabcc308c)
In 3-line graph, from e0 to e3, I want the path_dist: 'e3':['e0' 'v2', 'e3'}, 'e3': {'e0', 'v1', 'e3'}?

When drawing hypergraphs using the rubber band layout, node labels often overlap. This is especially prevalent when using longer strings as node labels; however, this problem also often occurs with very short labels as well. Due to the overlap, many labels are unreadable. Are there any settings within HyperNetX or Matplotlib to correct this? If not, are there any known workarounds? Thanks!

Describe the bug
When I import the library I get the following error
AttributeError: module 'numpy' has no attribute '_no_nep50_warning'

To Reproduce
On Google Colab with T4 GPU

!pip install hypernetx
import torch
import numpy as np
import networkx as nx
import sys
import hypernetx as hnx

Expected behavior
AttributeError: module 'numpy' has no attribute '_no_nep50_warning'

Screenshots

Hello, thank you for sharing this excellent library.

I want to compare the performance of clustering on weighted and unweighted hypergraphs as described in K. Hayashi, S. Aksoy, C. Park, H. Park, "Hypergraph random walks, Laplacians, and clustering".
I tried to do this based on Tutorial 11.
However, I think some modifications need.
h = hnx.Hypergraph(hnx.StaticEntitySet(data=data, weights=w))
I think this code does not make a weighted hypergraph.
Instead, this should be revised as below.
h = hnx.Hypergraph(hnx.StaticEntitySet(data=data, weights=w), weights=w)

Additionally, I cannot get the satisfying result that weighted hypergraphs perform better than unweighted ones.
Thus, I want to ask whether the below code is a correct way to make clustering on an unweighted hypergraph and evaluate clustering algorithms by NMI scores.

# get data
categories = all_categories[[1,15]]
twenty_train = fetch_20newsgroups(subset='test', categories=categories, shuffle=True, random_state=42)
doc_types=dict()
for i,x in enumerate(twenty_train.filenames):
    doc_types[i]=x.split('/')[-2]
tfidf_vect = TfidfVectorizer()
X_tfidf = tfidf_vect.fit_transform(twenty_train.data)

# construct hypergraph
mat = coo_matrix(X_tfidf)
edges = mat.col
nodes = mat.row
data = np.array([edges,nodes]).T
weights = mat.data
# clustering on weighted hypergraph
h = hnx.Hypergraph(hnx.StaticEntitySet(data=data,weights=weights),weights=weights)
clusters=hnx.spec_clus(h,num_clus,weights=True)
# clustering on unweighted hypergraph
uwh =  hnx.Hypergraph(hnx.StaticEntitySet(data=data,weights=None))
uw_clusters = hnx.spec_clus(uwh,num_clus,weights=False)

# construct clustering label list
categoryindexing = {}
_labels = {} # answer
for i in range(X_tfidf.shape[0]):
    if doc_types[i] not in categoryindexing:
        categoryindexing[doc_types[i]] = len(categoryindexing)
    ci = categoryindexing[doc_types[i]]
    _labels[i] = ci
labels = [_labels[i] for i in range(X_tfidf.shape[0])]
_w_pred = {} #  labels from weighted hypergraph
for i in clusters:
    for v in clusters[i]:
        _w_pred[v] = i
w_pred = [_w_pred[i] for i in range(X_tfidf.shape[0])]
_uw_pred = {} # labels from unweighted hypergraph
for i in uw_clusters:
    for v in uw_clusters[i]:
        _uw_pred[v] = i
uw_pred = [_uw_pred[i] for i in range(X_tfidf.shape[0])]

# evaluation on NMI score
from sklearn.metrics.cluster import normalized_mutual_info_score as NMI_SCORE
score = NMI_SCORE(labels, w_pred)
print(score) # 0.73
score = NMI_SCORE(labels, uw_pred)
print(score) # 0.78

First of all, I must say that it is very nice that the documentation of several functions comes with examples.

However, one of the first examples I tried did not complete after more than a 5 minute runtime (M2 MacBook Pro):

https://hypernetx.readthedocs.io/en/latest/algorithms/algorithms.html#algorithms.contagion.contagion_animation

When an example takes a very long time to run, this should be noted in the docs.

Ref: openjournals/joss-reviews#6016

When drawing a hypergraph with 95 nodes and 154 edges, many hyperedges are incorrectly encircling all of the 95 nodes. See the example provided below where no edges contain more than 3 nodes, but most of the 154 hyperedges are being drawn around all nodes.

Output:

Output (cropped):

import matplotlib.pyplot as plt
from hypernetx import *

edges = [
    ["10000546","10040991","10040998"],
    ["10000546","10040991","10043739"],
    ["10001708","10006436","10040991"],
    ["10001708","10021877","10040991"],
    ["10001708","10040991","10040998"],
    ["10001708","10040991","10046304"],
    ["10002086","10040991","10040998"],
    ["10002861","10040991","10040998"],
    ["10003018","10018073","10040991"],
    ["10003018","10040991","10040998"],
    ["10003216","10007963","10040991"],
    ["10003216","10011082","10040991"],
    ["10003216","10040991","10040998"],
    ["10003216","10040991","10057166"],
    ["10003816","10010761","10040991"],
    ["10003816","10012303","10040991"],
    ["10003816","10023213","10040991"],
    ["10003816","10040991","10040998"],
    ["10004994","10040991","10040998"],
    ["10004994","10040991","10041543"],
    ["10005908","10017977","10040991"],
    ["10005908","10018073","10040991"],
    ["10005908","10040991","10040998"],
    ["10005959","10013296","10040991"],
    ["10005959","10040991","10040998"],
    ["10006232","10006291","10040991"],
    ["10006232","10040991","10040998"],
    ["10006291","10040991","10040998"],
    ["10006436","10040991","10040998"],
    ["10007521","10040991","10040998"],
    ["10007963","10040991","10040998"],
    ["10009841","10040991","10040998"],
    ["10010761","10014982","10040991"],
    ["10010761","10040991","10040998"],
    ["10011082","10018073","10040991"],
    ["10011082","10040991","10040998"],
    ["10011082","10040991","10057166"],
    ["10011082","10040991","10079101"],
    ["10011954","10029305","10040991"],
    ["10011954","10040991","10040998"],
    ["10011954","10040991","10082206"],
    ["10012272","10040991","10040998"],
    ["10012272","10040991","10057167"],
    ["10012303","10040991","10040998"],
    ["10012375","10040991","10040998"],
    ["10012653","10034606","10040991"],
    ["10013296","10040991","10040998"],
    ["10013317","10040991","10040998"],
    ["10013317","10040991","10057166"],
    ["10014412","10017977","10040991"],
    ["10014412","10018073","10040991"],
    ["10014412","10038430","10040991"],
    ["10014412","10040991","10040998"],
    ["10014523","10040991","10040998"],
    ["10014982","10027665","10040991"],
    ["10014982","10040991","10040998"],
    ["10015917","10040991","10040998"],
    ["10017528","10017977","10040991"],
    ["10017528","10040991","10040998"],
    ["10017943","10040991","10040998"],
    ["10017969","10027665","10040991"],
    ["10017969","10040991","10040998"],
    ["10017977","10018027","10040991"],
    ["10017977","10018188","10040991"],
    ["10017977","10031013","10040991"],
    ["10017977","10040991","10040998"],
    ["10017977","10040991","10077546"],
    ["10017990","10017991","10040991"],
    ["10017990","10040991","10040998"],
    ["10017991","10040991","10040998"],
    ["10018012","10018073","10040991"],
    ["10018012","10040991","10040998"],
    ["10018027","10040991","10040998"],
    ["10018073","10019280","10040991"],
    ["10018073","10022114","10040991"],
    ["10018073","10040991","10040998"],
    ["10018073","10040991","10047066"],
    ["10018073","10040991","10068775"],
    ["10018073","10040991","10069888"],
    ["10018073","10040991","10074469"],
    ["10018073","10040991","10079101"],
    ["10018188","10021879","10040991"],
    ["10018188","10040991","10040998"],
    ["10018307","10040991","10040998"],
    ["10018424","10040991","10040998"],
    ["10018424","10040991","10057166"],
    ["10019231","10040991","10040998"],
    ["10019231","10040991","10047066"],
    ["10019280","10040991","10040998"],
    ["10019280","10040991","10057166"],
    ["10019654","10040991","10040998"],
    ["10019654","10040991","10047438"],
    ["10021877","10040991","10040998"],
    ["10021877","10040991","10046304"],
    ["10021879","10024970","10040991"],
    ["10021879","10040991","10040998"],
    ["10022114","10029305","10040991"],
    ["10022114","10040991","10040998"],
    ["10022396","10029305","10040991"],
    ["10022958","10040991","10040998"],
    ["10023213","10037546","10040991"],
    ["10023213","10040991","10040998"],
    ["10024324","10040991","10040998"],
    ["10024967","10040991","10040998"],
    ["10024970","10040991","10040998"],
    ["10025320","10040991","10040998"],
    ["10026753","10040991","10040998"],
    ["10027665","10040991","10040998"],
    ["10027946","10040991","10040998"],
    ["10028037","10040991","10040998"],
    ["10028302","10029317","10040991"],
    ["10028302","10040991","10040998"],
    ["10028377","10040991","10040998"],
    ["10028393","10040991","10040998"],
    ["10029107","10038666","10040991"],
    ["10029107","10040991","10040998"],
    ["10029305","10040991","10040998"],
    ["10029317","10040991","10040998"],
    ["10031013","10040991","10040998"],
    ["10033283","10038594","10040991"],
    ["10033283","10040991","10040998"],
    ["10034606","10040991","10040998"],
    ["10035227","10040991","10040998"],
    ["10037454","10038716","10040991"],
    ["10037454","10040991","10040998"],
    ["10037454","10040991","10057166"],
    ["10037546","10040991","10040998"],
    ["10037546","10040991","10057166"],
    ["10038430","10040991","10040998"],
    ["10038594","10040991","10040998"],
    ["10038666","10040991","10040998"],
    ["10038716","10040991","10040998"],
    ["10039628","10040991","10040998"],
    ["10039911","10040991","10040998"],
    ["10040792","10040991","10040998"],
    ["10040792","10040991","10047438"],
    ["10040798","10040991","10040998"],
    ["10040991","10040998"],
    ["10040991","10040998","10041543"],
    ["10040991","10040998","10043739"],
    ["10040991","10040998","10046304"],
    ["10040991","10040998","10046590"],
    ["10040991","10040998","10047066"],
    ["10040991","10040998","10047438"],
    ["10040991","10040998","10047635"],
    ["10040991","10040998","10057166"],
    ["10040991","10040998","10057167"],
    ["10040991","10040998","10068775"],
    ["10040991","10040998","10069888"],
    ["10040991","10040998","10074469"],
    ["10040991","10040998","10077546"],
    ["10040991","10040998","10079101"],
    ["10040991","10040998","10082206"],
    ["10040991","10047066","10074469"]
]

hpgph = Hypergraph(edges)

plt.figure(figsize=(80, 80))
draw(hpgph, ax=plt.subplot(111), with_edge_labels=True)

Perhaps https://arxiv.org/abs/2310.11626 can be added into the readme.

Depending on which OS is being used the following will print different results:

import hypernetx as hnx
import hypernetx.algorithms.hypergraph_modularity as hmod
formulaDict,stringTree = readFile("./CDL-FMINCE/toybox.dimacs") # code by me
H=formulaToHypergraph(formulaDict)
HDual = H.dual()
H=hmod.precompute_attributes(H)
HDual = hmod.precompute_attributes(HDual)

#this should diverge based off of OS

print(len(list(H.nodes())))
print(len(list(HDual.nodes())))

On the linux systems the results are 544 and 590, while on Windows I get 175 and 221. I suspect the singletons get removed by the attribute computation on windows, but not on the linux systems...

I was using ANTLR4 with python integration to parse a dimacs (SAT) file and interpret it as a hypergraph. The resulting graph has 544 nodes and 590 edges before the attributes are computed.

Compared Systems

Ubuntu system:

• Ubuntu 20.04.6 LTS
• wsl 2
• Python 3.8.10
• pip installation manager

Debian

• SMP Debian 5.10.127-1
• Python 3.9.2
• pip installation manager

Windows

• Windows 10 Home
• Python 3.11.3
• Anaconda virtual environment (+VSCode)

Good day sir,
how to create weighted directed hypergraph from hypernetx (code example).

Cordially,
Ghislain

I'm curious about the reasons. Is there any storage/computation inefficient problem?

Hi, this is a really good package :)

It would be loads more useful if you could provide support for weighted hypergraphs. It seems to be possible to create a weighted hypergraph at the moment like this:

import itertools
import hypernetx as hnx
import numpy as np

labels = "ABCD"
nodes = set(labels)
edges = list(
    itertools.chain(
        *[list(itertools.combinations(set(labels), ii)) for ii in range(2, len(labels))]
    )
)
hnx_edges = hnx.EntitySet(
    "Edges", 
    [hnx.Entity(
            "".join(ee), 
            ee,
            weight=np.random.rand() 
        ) for ee in edges]
)

h = hnx.Hypergraph(hnx_edges)
hnx.draw(h)
print(h.edges.elements)

but it's not possible to do any analysis with the weights as far as I can tell (see for example, the output of hnx.dist_stats(h) )

Are there any plans to add this type of functionality?

Thanks!

scenes = {
    0: ('FN', 'TH'),
    1: ('TH', 'JV'),
    2: ('BM', 'FN', 'JA'),
    3: ('JV', 'JU', 'CH', 'BM'),
    4: ('JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'),
    5: ('TH', 'GP'),
    6: ('GP', 'MP'),
    7: ('MA', 'GP')
}

H = hnx.Hypergraph(scenes)
H
hnx.to_pickle(H, filename='1.pkl')

File D:\Anaconda\envs\mhyperG\lib\site-packages\hypernetx\read_write.py:10, in to_pickle(obj, filename)
8 """Writes object to a pickle file"""
9 with open(f"{filename}", "wb") as f:
---> 10 pickle.dump(obj, f)

TypeError: 'NoneType' object is not callable
How can I fix the bug?

You have developed an awesome package, but is this package able to handle directed weighted hypernetworks.Thanks a lot!

Describe the bug

In (the compiled documentation file) hypergraph101.html there is an occurrence of :math:`s`-walk in the section "Walks Have Length and Width". I suspect that this is a syntax typo.

When I call print('qH =',hmod.modularity(H, K, strict)) it returns AttributeError: 'Entity' object has no attribute 'strength'

The full code:

import igraph as ig
import hypernetx as hnx
import hypernetx.algorithms.hypergraph_modularity as hmod

H = hnx.Hypergraph(lookup)
H = hmod.precompute_attributes(H)
#%%
K = hmod.kumar(H)
#%%
strict = hmod.strict
## Compute qH
print('qH =',hmod.modularity(H, K, strict))

Lookup is a dictionary of the form: {key1 : (hedge1, hedge2...), key2 : (hedge1, hedge3...)}

Describe the bug
The draw function overlaps edges, making it seem that they share nodes.

To Reproduce
Steps to reproduce the behavior:

1. Run the following code

import matplotlib.pyplot as plt
import hypernetx as hnx
articles = {
        'CA': ('1', '3', '5', '6', '13', '80', '92', '8', '24', '36', '63', '75', '86', '88', '91',
                '10', '33', '57', '84', '27', '50', '67', '18', '60', '69', '81', '23', '31', '45', '87',
                '4', '71', '16', '17', '30', '55', '62', '93', '2', '19', '48', '7', '14', '15', '44', '58'),
        'MRDM': ('1', '3', '5', '6', '13', '27', '38', '42', '67', '80',
                 '92', '9', '23', '29', '31', '34', '35', '45', '87', '88',
                 '33', '84', '18', '74', '98', '21', '82', '11', '19', '48',
                 '53', '61', '73', '85', '96', '97', '99', '7', '44', '95', '40', '76'),
        'DA': ('16', '17', '30', '62', '93', '50', '14', '15', '39', '40', '41', '76'),
        'Graphs': ('9', '23', '29', '31', '34', '35', '45', '87',), 
        'Onto': ('1', '3', '5', '6', '13', '27', '38', '42', '67', '80', '92', '50',), 
    }
H = hnx.Hypergraph(articles)
kwargs = {
        'with_node_labels': True,
        'with_node_counts': False,
        'with_edge_counts': False,
    }

hnx.drawing.draw(
    H_collapsed,
    edges_kwargs={
        'linewidths': 2,
    },
    nodes_kwargs={
    'facecolors': colors
    },
    edge_labels_kwargs={
        'fontsize': 16,
    },
    **kwargs)
plt.show()

2. See the result:
   
   As an example, the node 76 seems to be inside the edge Onto, but it's actually not.

Expected behavior
Edges should have node inside them only when they're actually part of them.

Environment (please complete the following information):

• OS: Linux
• Python Version Py3.9

Describe the bug

Most operations fail on empty hypergraph.

To Reproduce

hh = hnx.Hypergraph()
hn.draw(hh) # errors
hh.is_connected() # errors
hh.bipartite() # errors

Almost all functions / methods I tried threw a non-intuitive error.

HNX 2.1.3

Ref openjournals/joss-reviews#6016

Hi,
I understand that support for Weighted Hyperedges was asked in this issue.

However, the last question, was not answered. Could someone please answer it?

Additionally, has there been any update regarding the weighted hyperedges now that it is early 2022?

When I used algorithms.hypergraph_modularity.precompute_attributes , I realized that this function resets hyperedges weights to the unit weight. I seems that the problem comes from the first line H = HG.remove_singletons(). This function seems to not copy the original weights. When I delete this first line H = HG.remove_singletons() from the source code of precompute_attributes ,it works well.

Is it possible for edges with the same name but different nodes to coexist. Something like this :

{
  "is" : ["red","apple","rotten"],
  "is" : ["black","pen"]
}

How do you achieve this ?

I would like to store some edge properties. For hypernetx > 1.1 this should work.

For

H = hnx.Hypergraph()
H.add_edge(hnx.Entity('e1', {1, 2}, p='a property'))
H.edges['e1'].properties['p'] # returns 'a property'

I get what I expect. But for static hypergraphs, there are no properties:

H_static = H.convert_to_static(name='static')
H_static.edges.props # return {}

Am I doing something wrong?

Describe the bug

The title "Les Miserables" appears in Tutorial 1; it should be spelt with an eacute: Les Misérables

Possibly the bad orthography appears elsewhere too.

Until #137 is answered, I'll just ask questions here. I hope that's okay.

Can HNX represent isolated nodes? If yes, how can I construct a hypergraph with isolated nodes? If not, can you point to the section of the docs that explains this? I'm sure I'm missing something.

I noticed that when removing edges using .remove_edges() (docs missing), nodes with no remaining incident edges also disappear.

Ref: openjournals/joss-reviews#6016

When adding an existing node to an existing edge, the memberships property of the node stored in _nodes is not updated

Code to reproduce the issue:

system = hnx.Hypergraph()
edge0 = hnx.Entity('edge0')
edge1 = hnx.Entity('edge1')
node0 = hnx.Entity('node0')
node1 = hnx.Entity('node1')

system.add_edge(edge0)
system.add_edge(edge1)
system.add_node_to_edge(node0,edge0)
system.add_node_to_edge(node0,edge1)
system.add_node_to_edge(node1,edge1)

#Memberships seen from the nodes stored within _edges
print("Memberships seen from the nodes stored within _edges")
for el in system.edges:
    for edge in system.edges[el]:
        print(edge,system.edges[el][edge].memberships)
        
#Memberships seen from the nodes stored within _nodes
print("Memberships seen from the nodes stored within _nodes")
for el in system.nodes:
    print(el,system.nodes[el].memberships)

hnx.drawing.rubber_band.draw(system)

Hello,
I have the following error when I try to import hypernetx on Colab (with your tutorials) and also on my conda env:

ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-3-b7c66bb90caa> in <module>()
      3 import matplotlib.pyplot as plt
      4 import networkx as nx
----> 5 import hypernetx as hnx
      6 

4 frames
/usr/local/lib/python3.7/dist-packages/hypernetx/__init__.py in <module>()
      5 )
      6 from hypernetx.read_write import to_pickle, load_from_pickle
----> 7 from hypernetx.classes import *
      8 from hypernetx.reports import *
      9 from hypernetx.drawing import *

/usr/local/lib/python3.7/dist-packages/hypernetx/classes/__init__.py in <module>()
      1 from .entity import Entity, EntitySet
----> 2 from .hypergraph import Hypergraph
      3 from .staticentity import StaticEntity, StaticEntitySet

/usr/local/lib/python3.7/dist-packages/hypernetx/classes/hypergraph.py in <module>()
     11 from collections import OrderedDict, defaultdict
     12 from hypernetx.classes.entity import Entity, EntitySet
---> 13 from hypernetx.classes.staticentity import StaticEntity, StaticEntitySet
     14 from hypernetx.exception import HyperNetXError
     15 from hypernetx.utils.decorators import not_implemented_for

/usr/local/lib/python3.7/dist-packages/hypernetx/classes/staticentity.py in <module>()
      8 from hypernetx.exception import HyperNetXError
      9 from hypernetx.classes.entity import Entity, EntitySet
---> 10 from hypernetx.utils import HNXCount, DefaultOrderedDict, remove_row_duplicates
     11 from scipy.sparse import coo_matrix, csr_matrix, issparse
     12 import itertools as it

/usr/local/lib/python3.7/dist-packages/hypernetx/utils/__init__.py in <module>()
      2 from .decorators import not_implemented_for
      3 # from .toys import HarryPotter, LesMis, lesmis_hypergraph_from_df, book_tour, TransmissionProblem
----> 4 from .toys import *

ModuleNotFoundError: No module named 'hypernetx.utils.toys'

I'm doing something wrong?

Describe the bug

On Python 3.11 when i import hypernetx i get a few warnings:

>>> import hypernetx as hnx
/Users/drj/prj/HyperNetX/hypernetx/algorithms/homology_mod2.py:1: DeprecationWarning: invalid escape sequence '\{'
  """
/Users/drj/prj/HyperNetX/hypernetx/algorithms/homology_mod2.py:544: DeprecationWarning: invalid escape sequence '\m'
  """
/Users/drj/prj/HyperNetX/hypernetx/algorithms/homology_mod2.py:737: DeprecationWarning: invalid escape sequence '\{'
  """
/Users/drj/prj/HyperNetX/hypernetx/algorithms/laplacians_clustering.py:42: DeprecationWarning: invalid escape sequence '\g'
  """

These are causes by the TeX code appearing inside Python docstrings. Probably the simplest approach would be to make the docstrings raw by changing """ to r""" at the beginning of the affected docstrings [edit: as has been done in s_centrality_measures.py for example].

In addition the affected docstring in laplacians_clustering.py looks like it needs some dollar signs, but i am not familiar with your post-processing documentation tools.

Whilst i found this during my review for JOSS, i do not require it to be fixed for acceptance.

Environment (please complete the following information):

• macOS Darwin 20.6.0
• Python 3.11.4 (main, Jun 20 2023, 16:52:35) [Clang 13.0.0 (clang-1300.0.29.30)] on darwin

Thanks for this library.

One issue I am having a the moment is the absence of 'hypergraph_modularity' as per the following error:
AttributeError: module 'hypernetx.algorithms.hypergraph_modularity' has no attribute 'hypergraph_modularity'.

The various imports have been fulfilled:
import pandas as pd
import numpy as np
import pickle
import random

import igraph as ig
import hypernetx as hnx
import hypernetx.algorithms.hypergraph_modularity as hmod
import hypernetx.algorithms.generative_models as gm

Any assistance on this would be greatly appreciated.

I had to change the first line from

# !pip install hypernetx

to

!pip install hypernetx
!pip install celluloid
!pip install igraph

to get it to work

Steps to reproduce:

1. create a new google account
2. open the tutorial

I'm not familiar enough with codelab to give an easier reproduction than this, but I hope there is one.

EDIT: Another option to reproduce:

1. uninstall celluloid & igraph (and anything that depends on them)
2. run pip3 install hypernetx from terminal to trigger automatic installation of all of install_requires but not extras_require
3. run import hypernetx from python

I'm trying to build a hypergraph which has ~3.8M nodes and ~7.1M hyperedges.

My first attempt was to build a dictionary of edge -> nodelist so I could do a simple Hypergraph(dict) as per the docs, but that made no apparent progress.

I then tried to add the edges, then add the nodes. But after ~8 minutes I'm was only at 48000 edges and based on log output the process was getting markedly slower as the hypergraph grew.

I'm now trying an approach where I create a list of Entity objects then add them to an EntitySet, but that latter step has been going for many minutes now.

Skimming the code it seems like all of these bottom out in for loops that add elements, often with several uniqueness tests.

Is there an alternate construction method that I'm missing? Or am I just outside the design bounds trying to build a hypergraph this large?

Installing hypernetx in a clean virtual environment fails due to a missing package: decorator.

Step to reproduce (after initializing a new empty virtual environment):

pip install hypernetx

giving an error like this:

>>> import hypernetx as hnx
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/tmp/hnx-version/venv/lib/python3.8/site-packages/hypernetx/__init__.py", line 7, in <module>
    from hypernetx.classes import *
  File "/tmp/hnx-version/venv/lib/python3.8/site-packages/hypernetx/classes/__init__.py", line 2, in <module>
    from .hypergraph import Hypergraph
  File "/tmp/hnx-version/venv/lib/python3.8/site-packages/hypernetx/classes/hypergraph.py", line 13, in <module>
    from hypernetx.classes.staticentity import StaticEntity, StaticEntitySet
  File "/tmp/hnx-version/venv/lib/python3.8/site-packages/hypernetx/classes/staticentity.py", line 10, in <module>
    from hypernetx.utils import (
  File "/tmp/hnx-version/venv/lib/python3.8/site-packages/hypernetx/utils/__init__.py", line 8, in <module>
    from .decorators import not_implemented_for
  File "/tmp/hnx-version/venv/lib/python3.8/site-packages/hypernetx/utils/decorators.py", line 4, in <module>
    from decorator import decorator
ModuleNotFoundError: No module named 'decorator'

Of course, installing the decorator package manually fixes the issue and hypernetx imports fine. I think the decorator package should just be added to the install_requires entry in setup.cfg and setup.py since this package is required to even load the package at all.

Tested using Python versions 3.8 and 3.9, using hypernetx 1.2.2 (on Ubuntu).

Hi there,

I am experiencing a weird behaviour when retrieving the nodelist from the Hypergraph. It is so basic that I do wonder if, rather than being an issue, I am doing something wrong or if I am missing some option, but I can't figure it out.

Take, for instance, the first tutorial notebook. When one runs H.nodes, one is returned EntitySet(:Nodes,['BM', 'JU', 'MP', 'CN', 'TH', 'FN', 'JA', 'BR', 'GP', 'MA', 'JV', 'CH', 'CC'],{'weight': 1.0}). Notice the ordering of the nodes: that is the same order of, for instance, the H.adjacency_matrix() matrix.

On the other hand, if one runs list(H.nodes) or H.nodes.elements or even prints the nodes in a for node in H.nodes: loop, one gets a completely different ordering: ['FN', 'TH', 'JV', 'BM', 'JA', 'JU', 'CH', 'BR', 'CN', 'CC', 'GP', 'MP', 'MA'].

What am I missing? I think this is confusing, to say the least.

Thanks in advance,
G

Is your feature request related to a problem? Please describe.

Currently, class signatures in the documentation are hard to read due to type annotations crowding:

In the example above, there are so many type annotations that it is hard to find the second paramater data

Describe the solution you'd like

Because the parameters are already described in the docstring, I belive that hiding the type annotations would lead to a better documentation. Sometimes less is more. The parameters for the class constructors should be obvious.

Additional context

openjournals/joss-reviews#6016, I do not consider this issue blocking for acceptance. But it would be a good improvement

Does HyperNetX have a support forum or chatroom that is more appropriate for general discussion and usage questions than GitHub issues? Or do you prefer such questions to be asked in the issue tracker?