Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Overview

Version repo size Arxiv build badge coverage badge benedekrozemberczki


Little Ball of Fur is a graph sampling extension library for Python.

Please look at the Documentation, relevant Paper, Promo video and External Resources.

Little Ball of Fur consists of methods that can sample from graph structured data. To put it simply it is a Swiss Army knife for graph sampling tasks. First, it includes a large variety of vertex, edge, and exploration sampling techniques. Second, it provides a unified application public interface which makes the application of sampling algorithms trivial for end-users. Implemented methods cover a wide range of networking (Networking, INFOCOM, SIGCOMM) and data mining (KDD, TKDD, ICDE) conferences, workshops, and pieces from prominent journals.


Citing

If you find Little Ball of Fur useful in your research, please consider citing the following paper:

@inproceedings{littleballoffur,
               title={{Little Ball of Fur: A Python Library for Graph Sampling}},
               author={Benedek Rozemberczki and Oliver Kiss and Rik Sarkar},
               year={2020},
               pages = {3133–3140},
               booktitle={Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20)},
               organization={ACM},
}

A simple example

Little Ball of Fur makes using modern graph subsampling techniques quite easy (see here for the accompanying tutorial). For example, this is all it takes to use Diffusion Sampling on a Watts-Strogatz graph:

import networkx as nx
from littleballoffur import DiffusionSampler

graph = nx.newman_watts_strogatz_graph(1000, 20, 0.05)

sampler = DiffusionSampler()

new_graph = sampler.sample(graph)

Methods included

In detail, the following sampling methods were implemented.

Node Sampling

Edge Sampling

Exploration Based Sampling

Head over to our documentation to find out more about installation and data handling, a full list of implemented methods, and datasets. For a quick start, check out our examples.

If you notice anything unexpected, please open an issue and let us know. If you are missing a specific method, feel free to open a feature request. We are motivated to constantly make Little Ball of Fur even better.


Installation

Little Ball of Fur can be installed with the following pip command.

$ pip install littleballoffur

As we create new releases frequently, upgrading the package casually might be beneficial.

$ pip install littleballoffur --upgrade

Running examples

As part of the documentation we provide a number of use cases to show how to use various sampling techniques. These can accessed here with detailed explanations.

Besides the case studies we provide synthetic examples for each model. These can be tried out by running the scripts in the examples folder. You can try out the random walk sampling example by running:

$ cd examples
$ python ./exploration_sampling/randomwalk_sampler.py

Running tests

$ python setup.py test

License

Comments
  • change initial num of nodes formula

    change initial num of nodes formula

    to avoid having more initial nodes than the requested final number of nodes (when the final number of nodes requested is much smaller than the graph size).

    opened by bricaud 7
  • Error install dependency networkit==7.1

    Error install dependency networkit==7.1

    I didn't manage to install littleballoffur due to one of its dependency that seems outdated. It didn't work to install networkit==7.1 but I did manage to run its latest version. However, littleballoffur runs on networkit==7.1.

    I am using a Jupyter notebook as an environment and the following system specs: posix Darwin 21.4.0 3.8.12 (default, Mar 17 2022, 14:54:15) [Clang 13.0.0 (clang-1300.0.29.30)]

    The specific error output:

    Collecting networkit==7.1
      Using cached networkit-7.1.tar.gz (3.1 MB)
      Preparing metadata (setup.py) ... error
      error: subprocess-exited-with-error
      
      × python setup.py egg_info did not run successfully.
      │ exit code: 1
      ╰─> [2 lines of output]
          ERROR: No suitable compiler found. Install any of these:  ['g++', 'g++-8', 'g++-7', 'g++-6.1', 'g++-6', 'g++-5.3', 'g++-5.2', 'g++-5.1', 'g++-5', 'g++-4.9', 'g++-4.8', 'clang++', 'clang++-3.8', 'clang++-3.7']
          If using AppleClang, OpenMP might be needed. Install with: 'brew install libomp'
          [end of output]
      
      note: This error originates from a subprocess, and is likely not a problem with pip.
    error: metadata-generation-failed
    
    × Encountered error while generating package metadata.
    ╰─> See above for output.
    
    note: This is an issue with the package mentioned above, not pip.
    hint: See above for details.
    
    

    Please note that: libomp 14.0.0 is already installed and up-to-date.

    Is there some way I could install the library on networkit v10? Thanks a lot!

    opened by CristinBSE 6
  • Node attributes are not copied from original graph

    Node attributes are not copied from original graph

    Breadth and Depth First Search return me subgraphs without correct attributes on nodes/edges. Actually, I found that the dict containing those attributes has been completely deleted in the sampled graph. Is this a known issue? Is the sampler supposed to work in this way?

    opened by jungla88 6
  • Why can't I use the graph imported by nx.read_edgelist()

    Why can't I use the graph imported by nx.read_edgelist()

    graph = nx.read_edgelist("filename", nodetype=int, data=(("Weight", int),))

    error : AssertionError: Graph is not connected. why? 'graph' is a networkx graph

    opened by DeathSentence 5
  • Spikyball exploration sampling

    Spikyball exploration sampling

    You might find the change a bit invasive (understandable :) This adds a new family exploration sampling method (spikyball) described in the paper Spikyball sampling: Exploring large networks via an inhomogeneous filtered diffusion available here https://arxiv.org/abs/2010.11786 and submitted for publication in Combinatorial Optimization, Graph, and Network Algorithms journal. The version number has been increased in order not to collide with official releases of lbof, you might want to change this...

    opened by naspert 4
  • Assumptions on graph properties

    Assumptions on graph properties

    Hi there,

    I am wondering if it would be possible to relax some constrain the graph has to satisfy in order to start an exploration on it. In particular, the requirement of connectivity seems a bit strong to me. I think a graph sampling procedure could easily deal with such property, since in the case the graph is not connected the sampling could take place on the single connected components or the exploration could rely on the neighborhood of the current node explored. For node sampling strategies like BFS and DFS looks pretty natural to me, also for Random Walk Sampling (maybe the one with the restart probability could be a little tricky). Something strange could probably happen for edge sampling if the connectivity property is not satisfied. Do you see any possibility to extend little ball of fur to such type of graphs? What was the reason that bring you to assume the connectivity property for graphs?

    Thank you !

    opened by jungla88 3
  • Error importing DiffusionSampler

    Error importing DiffusionSampler

    Hello,

    First of all, thank you for your great work building this library. Great extension to NetworkX.

    I am facing an issue when trying to import the DiffusionSampler specifically. All the other samplers get imported just fine. However the DiffusionSampler raises import issue.

    I am using a Jupyter notebook as an environment.

    The specific error output:

    ---------------------------------------------------------------------------
    ImportError                               Traceback (most recent call last)
    <ipython-input-29-fbd222d9c756> in <module>
    ----> 1 from littleballoffur import DiffusionSampler
          2 
          3 
          4 model = DiffusionSampler()
          5 new_graph = model.sample(wd50k_connected_relabeled)
    
    ImportError: cannot import name 'DiffusionSampler' from 'littleballoffur'
    

    Is this replicable?

    Thank you in advance for looking into it.

    opened by DimitrisAlivas 3
  • ForestFireSampler throws exceptions for some seed values

    ForestFireSampler throws exceptions for some seed values

    Hi,

    I am trying to sample an undirected, connected graph of 5559 nodes and 10804 edges into a sample of 100 nodes. As I loop over the "creation of samples" part, I am altering the seed for the ForeFireSampler every time to obtain a different sample.

    E.g. seed_value = random.randint(1,2147483646) sampler = ForestFireSampler(100, seed=seed_value )

    However, for some runs I get an exception thrown, which is also reproducible. I assume it is related to specific seed values which the sampler doesn´t seem to be able to handle. An example is seed value 1176372277.

    Traceback (most recent call last): File "/project/topology_extraction.py", line 472, in abstraction_G = graph_sampling(S) File "/project/topology_extraction.py", line 234, in graph_sampling new_graph = sampler.sample(S) File "/usr/local/lib/python3.8/dist-packages/littleballoffur/exploration_sampling/forestfiresampler.py", line 74, in sample self._start_a_fire(graph) File "/usr/local/lib/python3.8/dist-packages/littleballoffur/exploration_sampling/forestfiresampler.py", line 47, in _start_a_fire top_node = node_queue.popleft() IndexError: pop from an empty deque

    Process finished with exit code 1

    I believe this is a bug in the library.

    Thanks! Nils

    opened by nrodday 3
  • Error in forest fire sampling

    Error in forest fire sampling

    Hi,

    While running the forest fire sampling code, I got an error that it is trying to pop an element from an empty deque.

    File "/opt/anaconda3/lib/python3.7/site-packages/littleballoffur/exploration_sampling/forestfiresampler.py", line 47, in _start_a_fire top_node = node_queue.popleft() IndexError: pop from an empty deque

    I am not sure if it was due to data or needs an empty/try-catch check or should it be handled by application code. Hence opened an issue.

    Thank you

    opened by apurvamulay 2
  • Broken link in Readme (readdthedocs)

    Broken link in Readme (readdthedocs)

    https://littleballoffur.readthedocs.io/en/latest/notes/introduction.html

    as of 2020-05-18 9:35 AM EDT, it says "sorry this page does not exist"

    opened by bbrewington 1
  • Error in line 254 _checking_indexing() of backend.py

    Error in line 254 _checking_indexing() of backend.py

    According to your code, once numeric_indices != node_indices, the error raises. Under my scenario, I constructed a networkx graph in which the indices of nodes start from '1', and then, the sampler did not work. This error will be triggered if the indices of nodes in a networkx graph do not start from '0'. I have to adjust my graph such that the indices of nodes start from '0' to utilize your samplers. I hope you can refine this part of the code to avoid someone else meets this problem.

    opened by Haoran-Young 0
Releases(v_20200)
Owner
Benedek Rozemberczki
Machine Learning Engineer at AstraZeneca and PhD candidate at The University of Edinburgh.
Benedek Rozemberczki
Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

Spark Python Notebooks This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, fro

Jose A Dianes 1.5k Jan 02, 2023
cleanlab is the data-centric ML ops package for machine learning with noisy labels.

cleanlab is the data-centric ML ops package for machine learning with noisy labels. cleanlab cleans labels and supports finding, quantifying, and lear

Cleanlab 51 Nov 28, 2022
MiniTorch - a diy teaching library for machine learning engineers

This repo is the full student code for minitorch. It is designed as a single repo that can be completed part by part following the guide book. It uses

1.1k Jan 07, 2023
Simulate & classify transient absorption spectroscopy (TAS) spectral features for bulk semiconducting materials (Post-DFT)

PyTASER PyTASER is a Python (3.9+) library and set of command-line tools for classifying spectral features in bulk materials, post-DFT. The goal of th

Materials Design Group 4 Dec 27, 2022
Predict profitability of trades based on indicator buy / sell signals

Predict profitability of trades based on indicator buy / sell signals Trade profitability analysis for trades based on various indicators signals: MAC

Tomasz Porzycki 1 Dec 15, 2021
Simple linear model implementations from scratch.

Hand Crafted Models Simple linear model implementations from scratch. Table of contents Overview Project Structure Getting started Citing this project

Jonathan Sadighian 2 Sep 13, 2021
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and many other libraries. Documenta

2.5k Jan 07, 2023
Programming assignments and quizzes from all courses within the Machine Learning Engineering for Production (MLOps) specialization offered by deeplearning.ai

Machine Learning Engineering for Production (MLOps) Specialization on Coursera (offered by deeplearning.ai) Programming assignments from all courses i

Aman Chadha 173 Jan 05, 2023
Distributed scikit-learn meta-estimators in PySpark

sk-dist: Distributed scikit-learn meta-estimators in PySpark What is it? sk-dist is a Python package for machine learning built on top of scikit-learn

Ibotta 282 Dec 09, 2022
Python based GBDT implementation

Py-boost: a research tool for exploring GBDTs Modern gradient boosting toolkits are very complex and are written in low-level programming languages. A

Sberbank AI Lab 20 Sep 21, 2022
A Streamlit demo to interactively visualize Uber pickups in New York City

Streamlit Demo: Uber Pickups in New York City A Streamlit demo written in pure Python to interactively visualize Uber pickups in New York City. View t

Streamlit 230 Dec 28, 2022
Customers Segmentation with RFM Scores and K-means

Customer Segmentation with RFM Scores and K-means RFM Segmentation table: K-Means Clustering: Business Problem Rule-based customer segmentation machin

5 Aug 10, 2022
A Python toolbox to churn out organic alkalinity calculations with minimal brain engagement.

Organic Alkalinity Sausage Machine A Python toolbox to churn out organic alkalinity calculations with minimal brain engagement. Getting started To mak

Charles Turner 1 Feb 01, 2022
Magenta: Music and Art Generation with Machine Intelligence

Magenta is a research project exploring the role of machine learning in the process of creating art and music. Primarily this involves developing new

Magenta 18.1k Dec 30, 2022
Bottleneck a collection of fast, NaN-aware NumPy array functions written in C.

Bottleneck Bottleneck is a collection of fast, NaN-aware NumPy array functions written in C. As one example, to check if a np.array has any NaNs using

Python for Data 835 Dec 27, 2022
vortex particles for simulating smoke in 2d

vortex-particles-method-2d vortex particles for simulating smoke in 2d -vortexparticles_s

12 Aug 23, 2022
All-in-one web-based development environment for machine learning

All-in-one web-based development environment for machine learning Getting Started • Features & Screenshots • Support • Report a Bug • FAQ • Known Issu

3 Feb 03, 2021
A quick reference guide to the most commonly used patterns and functions in PySpark SQL

Using PySpark we can process data from Hadoop HDFS, AWS S3, and many file systems. PySpark also is used to process real-time data using Streaming and

Sundar Ramamurthy 53 Dec 21, 2022
Iris-Heroku - Putting a Machine Learning Model into Production with Flask and Heroku

Puesta en Producción de un modelo de aprendizaje automático con Flask y Heroku L

Jesùs Guillen 1 Jun 03, 2022
Python ML pipeline that showcases mltrace functionality.

mltrace tutorial Date: October 2021 This tutorial builds a training and testing pipeline for a toy ML prediction problem: to predict whether a passeng

Log Labs 28 Nov 09, 2022