Comcomai ainize

Find nodes that has probability of joining neigbors

This repository provides ten community detection methods The using each methods is done in the following steps:

1. User types the argument which is composed of population, neighbors, probability, algorithm
2. User queries ainize server with the Num 1's arguments
3. ainize-node-join-neighbors Ainized server return a dictionary.

How to deploy

ainize-node-join-neighbors server is dockerlized, so it can be built and run using docker commands.

Docker build

$ docker build -t [your name]/[your docker repo] .

Docker run

$ docker run -p 80:80 -d [your name]/[your docker repo]

Now the server is available at http://localhost. To learn how to query the server, see the next section

Note that the docker image can be deployed using any docker-based deploy platform ainize.ai

How to query

http://localhost/?population=[input integer number]&neighbors=[input integer number]&probability=[input float number]&alogorithm=[input method] 

Note that

1. population is enough larger than neighbors e.g population = 100 , neighbors = 20
2. probability less than or equal to 1
3. There are 10 methods walklets, deepWalk, splitter, edmot, danmf, mnmf, labelPropagation, graRep, graphWave, nnsed
4. In this paper, nnsed is superior method than other detection methods so I recommend using nnsed method first.

References

Original code & paper

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Documentation | Paper

Karate Club is an unsupervised machine learning extension library for NetworkX.

Karate Club consists of state-of-the-art methods to do unsupervised learning on graph structured data. To put it simply it is a Swiss Army knife for small-scale graph mining research. First, it provides network embedding techniques at the node and graph level. Second, it includes a variety of overlapping and non-overlapping commmunity detection methods. The package also includes methods that can deal with bipartite, temporal and heterogeneous graphs. Implemented methods cover a wide range of network science (NetSci, Complenet), data mining (ICDM, CIKM, KDD), artificial intelligence (AAAI, IJCAI) and machine learning (NeurIPS, ICML, ICLR) conferences and pieces from prominent journals.

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Citing

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

@misc{rozemberczki2020karateclub,
title = {Karate Club: A tool for unsupervised learning on graph structured data.},
author = {Benedek Rozemberczki and Rik Sarkar},
year = {2020} }

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A simple example

Karate Club makes the use of modern community detection tecniques quite easy (see here for the accompanying tutorial). For example, this is all it takes to use on a Watts-Strogatz graph Ego-splitting:

import networkx as nx
from karateclub import EgoNetSplitter

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

splitter = EgoNetSplitter(1.0)

splitter.fit(g)

print(splitter.overlapping_partitions)

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Models included

In detail, the following methods are currently implemented.

Overlapping Community Detection

• DANMF from Ye et al.: Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection (CIKM 2018)

• M-NMF from Wang et al.: Community Preserving Network Embedding (AAAI 2017)

• Ego-Splitting from Epasto et al.: Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters (KDD 2017)

• NNSED from Sun et al.: A Non-negative Symmetric Encoder-Decoder Approach for Community Detection (CIKM 2017)

Non-Overlapping Community Detection

• EdMot from Li et al.: EdMot: An Edge Enhancement Approach for Motif-aware Community Detection (KDD 2019)

• Label Propagation from Raghavan et al.: Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks (Physics Review E 2007)

Neighbourhood-Based Node Level Embedding

• Walklets from Perozzi et al.: Don't Walk, Skip! Online Learning of Multi-scale Network Embeddings (ASONAM 2017)

• GraRep from Cao et al.: GraRep: Learning Graph Representations with Global Structural Information (CIKM 2015)

• DeepWalk from Perozzi et al.: DeepWalk: Online Learning of Social Representations (KDD 2014)

Structural Node Level Embedding

• GraphWave from Donnat et al.: Learning Structural Node Embeddings via Diffusion Wavelets (KDD 2018)

Graph Level Embedding

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 Karate Club even better.

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Installation

$ pip install karateclub

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Running examples

$ python examples.py