benedekrozemberczki/MUSAE benedekrozemberczki/MUSAE

  • Stars
    star
    136
  • Rank 258,974 (Top 6 %)
  • Language
    Python
  • License
    GNU General Publi...
  • Created about 5 years ago
  • Updated over 1 year ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
benedekrozemberczki/MUSAE
github

benedekrozemberczki

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

The reference implementation of "Multi-scale Attributed Node Embedding". (Journal of Complex Networks 2021)

MUSAE

Arxiv codebeat badge repo size benedekrozemberczki

The reference implementation of Multi-Scale Attributed Node Embedding. (Journal of Complex Networks 2021)

Abstract

We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE). Capturing attribute-neighborhood relationships over multiple scales is useful for a diverse range of applications, including latent feature identification across disconnected networks with similar attributes. We prove theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings. Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social, web and citation network datasets.

The second-order random walks sampling methods were taken from the reference implementation of Node2Vec.

The datasets are also available on SNAP.

The model is now also available in the package Karate Club.

This repository provides the reference implementations for MUSAE and AE as described in the paper:

Multi-Scale Attributed Node Embedding. Benedek Rozemberczki, Carl Allen, and Rik Sarkar. Journal of Complex Networks 2021

Table of Contents

  1. Citing
  2. Requirements
  3. Datasets
  4. Logging
  5. Options
  6. Examples

Citing

If you find MUSAE useful in your research, please consider citing the following paper:

>@article{musae,
          author = {Rozemberczki, Benedek and Allen, Carl and Sarkar, Rik},
          title = {{Multi-Scale Attributed Node Embedding}},
          journal = {Journal of Complex Networks},
          volume = {9},
          number = {2},
          year = {2021},
}

Requirements

The codebase is implemented in Python 3.5.2. package versions used for development are just below.

networkx          2.4
tqdm              4.28.1
numpy             1.15.4
pandas            0.23.4
texttable         1.5.0
scipy             1.1.0
argparse          1.1.0
gensim            3.6.0

Datasets

Logging

The models are defined in a way that parameter settings and runtimes are logged. Specifically we log the followings:

1. Hyperparameter settings.     We save each hyperparameter used in the experiment.
2. Optimization runtime.        We measure the time needed for optimization - measured by seconds.
3. Sampling runtime.            We measure the time needed for sampling - measured by seconds.

Options

Learning the embedding is handled by the src/main.py script which provides the following command line arguments.

Input and output options

  --graph-input      STR   Input edge list csv.     Default is `input/edges/chameleon_edges.csv`.
  --features-input   STR   Input features json.     Default is `input/features/chameleon_features.json`.
  --output           STR   Embedding output path.   Default is `output/chameleon_embedding.csv`.
  --log              STR   Log output path.         Default is `logs/chameleon.json`.

Random walk options

  --sampling      STR       Random walker order (first/second).              Default is `first`.
  --P             FLOAT     Return hyperparameter for second-order walk.     Default is 1.0
  --Q             FLOAT     In-out hyperparameter for second-order walk.     Default is 1.0.
  --walk-number   INT       Walks per source node.                           Default is 5.
  --walk-length   INT       Truncated random walk length.                    Default is 80.

Model options

  --model                 STR        Pooled or multi-scale model (AE/MUSAE).      Default is `musae`.
  --base-model            STR        Use of Doc2Vec base model.                   Default is `null`.
  --approximation-order   INT        Matrix powers approximated.                  Default is 3.
  --dimensions            INT        Number of dimensions.                        Default is 32.
  --down-sampling         FLOAT      Length of random walk per source.            Default is 0.001.
  --exponent              FLOAT      Downsampling exponent of frequency.          Default is 0.75.
  --alpha                 FLOAT      Initial learning rate.                       Default is 0.05.
  --min-alpha             FLOAT      Final learning rate.                         Default is 0.025.
  --min-count             INT        Minimal occurence of features.               Default is 1.
  --negative-samples      INT        Number of negative samples per node.         Default is 5.
  --workers               INT        Number of cores used for optimization.       Default is 4.
  --epochs                INT        Gradient descent epochs.                     Default is 5.

Examples

Training a MUSAE model for a 10 epochs.

$ python src/main.py --epochs 10

Changing the dimension size.

$ python src/main.py --dimensions 32

License

More Repositories

1

awesome-graph-classification

A collection of important graph embedding, classification and representation learning papers with implementations.
Python
4,666
star
2

pytorch_geometric_temporal

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
Python
2,455
star
3

awesome-decision-tree-papers

A collection of research papers on decision, classification and regression trees with implementations.
Python
2,248
star
4

awesome-community-detection

A curated list of community detection research papers with implementations.
Python
2,224
star
5

karateclub

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Python
2,065
star
6

awesome-fraud-detection-papers

A curated list of data mining papers about fraud detection.
Python
1,481
star
7

CapsGNN

A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).
Python
1,216
star
8

awesome-gradient-boosting-papers

A curated list of gradient boosting research papers with implementations.
Python
966
star
9

graph2vec

A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).
Python
860
star
10

ClusterGCN

A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
Python
757
star
11

littleballoffur

Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)
Python
676
star
12

SimGNN

A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).
Python
657
star
13

awesome-monte-carlo-tree-search-papers

A curated list of Monte Carlo tree search papers with implementations.
Python
565
star
14

datasets

A repository of pretty cool datasets that I collected for network science and machine learning research.
551
star
15

GraphWaveletNeuralNetwork

A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)
Python
548
star
16

MixHop-and-N-GCN

An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).
Python
395
star
17

APPNP

A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).
Python
351
star
18

AttentionWalk

A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).
Python
309
star
19

SGCN

A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).
Python
262
star
20

GAM

A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).
Python
261
star
21

GEMSEC

The TensorFlow reference implementation of 'GEMSEC: Graph Embedding with Self Clustering' (ASONAM 2019).
Python
244
star
22

SEAL-CI

A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)
Python
204
star
23

shapley

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
Python
203
star
24

Splitter

A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).
Python
203
star
25

DANMF

A sparsity aware implementation of "Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection" (CIKM 2018).
Python
194
star
26

GraphWaveMachine

A scalable implementation of "Learning Structural Node Embeddings Via Diffusion Wavelets (KDD 2018)".
Python
176
star
27

role2vec

A scalable Gensim implementation of "Learning Role-based Graph Embeddings" (IJCAI 2018).
Python
158
star
28

EdMot

An implementation of "EdMot: An Edge Enhancement Approach for Motif-aware Community Detection" (KDD 2019)
Python
128
star
29

M-NMF

An implementation of "Community Preserving Network Embedding" (AAAI 2017)
Python
119
star
30

diff2vec

Reference implementation of Diffusion2Vec (Complenet 2018) built on Gensim and NetworkX.
Python
117
star
31

LabelPropagation

A NetworkX implementation of Label Propagation from a "Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks" (Physical Review E 2008).
Python
111
star
32

walklets

A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).
Python
98
star
33

tigerlily

TigerLily: Finding drug interactions in silico with the Graph.
Jupyter Notebook
95
star
34

BANE

A sparsity aware implementation of "Binarized Attributed Network Embedding" (ICDM 2018).
Python
85
star
35

EgoSplitting

A NetworkX implementation of "Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters" (KDD 2017).
Python
80
star
36

ASNE

A sparsity aware and memory efficient implementation of "Attributed Social Network Embedding" (TKDE 2018).
Python
77
star
37

TENE

A sparsity aware implementation of "Enhanced Network Embedding with Text Information" (ICPR 2018).
Python
71
star
38

SINE

A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018).
Python
69
star
39

RolX

An alternative implementation of Recursive Feature and Role Extraction (KDD11 & KDD12)
Python
58
star
40

GraRep

A SciPy implementation of "GraRep: Learning Graph Representations with Global Structural Information" (WWW 2015).
Python
58
star
41

PDN

The official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)
Python
55
star
42

TADW

An implementation of "Network Representation Learning with Rich Text Information" (IJCAI '15).
Python
54
star
43

spatiotemporal_datasets

Spatiotemporal datasets collected for network science, deep learning and general machine learning research.
43
star
44

NMFADMM

A sparsity aware implementation of "Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence" (ICASSP 2014).
Python
40
star
45

FEATHER

The reference implementation of FEATHER from the CIKM '20 paper "Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models".
Python
40
star
46

BoostedFactorization

An implementation of "Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation" (ASONAM 2019).
Python
33
star
47

resolutions-2019

A list of data mining and machine learning papers that I implemented in 2019.
20
star
48

OrbitalFeatures

A sparsity aware implementation of "Biological Network Comparison Using Graphlet Degree Distribution" (Bioinformatics 2007)
Python
19
star
49

FSCNMF

An implementation of "Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks".
Python
18
star
50

GRAF

Inner product natural graph factorization machine used in 'GEMSEC: Graph Embedding with Self Clustering' .
Python
10
star
51

HullCoverConditionedUnitDiskGraph

A generator for unit disk graphs conditioned on concave hull cover.
Python
8
star
52

AV_Ultimate_Student_Hunt

Solution for the Ultimate Student Hunt Challenge (1st place).
R
8
star
53

NestedSubtreeHash

A distributed implementation of "Nested Subtree Hash Kernels for Large-Scale Graph Classification Over Streams" (ICDM 2012).
Python
7
star
54

Societe-General

Solution for ENS - Societe Generale Challenge (1st place).
R
5
star
55

resolutions-2020

4
star
56

graphmining.ai

Benedek Rozemberczki Personal Webpage
4
star
57

benedekrozemberczki

3
star