Network Scanner

quick analysis network data based on networkit.

Download

git clone https://github.com/carlons/network_scanner.git

Install

cd dist/

sudo pip3 install network_scanner-0.1.0-py3-none-any.whl

Usage

Input format

1. Network data file

   Only support edgelist format. Each row has two columns which seperated by tab. The first column is source id, and the second column is target id.

   For example:

   1 2

   1 33

   3 42

   45 67

   ...

2. Index file:

   This file records the name of each node in the network. It has two columns which seperated by tab. The first column is node id, and the second column is node name. For example:

   1 'hello'

   2 'world'

   3 'three'

   33 'John'

   42 'Ramos'

   45 'Ronaldo'

   67 'Ozil'

   ...

Function example

Complete codes are in example.py.

First import necessary moudle.

import network_scanner
from network_scanner.networkit_based import degree_analysis
from network_scanner.networkit_based import calculate_betweeness
from network_scanner.networkit_based import centrality_analysis
from network_scanner.networkit_based import networkit_util
from network_scanner.networkit_based import networkit_analysis

Then set global parameters.

filepath = './input/toy_graph'
label = 'toy'
outpath = './output/' + label + '/'

Now we can scan this network.

1. general analysis

  networkit_analysis.analysis(filepath, label, outpath, directed=False)
  networkit_analysis.analysis(filepath, label, outpath, directed=True)

2. degree analysis

• get top k's name

  false_id_value_filename = outpath + 'toy-falseid-degree'
  map_filename = outpath + 'undirected-toy-map-node-ids'
  index_filename = './input/index'
  result_filename = outpath + label + 'degree-top-k-trueid-value'
  k = 3
  paras = {'false_id_value_filename': false_id_value_filename, 'map_filename': map_filename,
            'index_filename': index_filename, 'result_filename': result_filename, 'k': k
  degree_analysis.get_top_k_trueid_and_name(**paras)

• fit powerlaw distribution

  net = networkit_util.readnetwork(filepath, directed=False)
  degree_analysis.get_deg_seq(net, label, outpath, degree_type='all')
  degree_seq_filename = outpath + 'toy-all-degree'
  degree_analysis.power_law_analysis(degree_seq_filename, label, outpath, degree_type='all')

• indegree vs outdegree

  indeg_falseid_value_filename = outpath + 'toy-falseid-indegree'
  outdeg_falseid_value_filename = outpath + 'toy-falseid-outdegree'
  map_filename = outpath + 'directed-toy-map-node-ids'
  index_filename = './input/index'
  result_filename = outpath + label + 'indeg-vs-outdeg'
  paras = {'indeg_falseid_value_filename': indeg_falseid_value_filename,
           'outdeg_falseid_value_filename': outdeg_falseid_value_filename, 'map_filename': map_filename,
            'index_filename': index_filename, 'result_filename': result_filename}
  degree_analysis.get_top_in_out_degree_cmp(**paras)

3. distance analysis

```
net = networkit_util.readnetwork(filepath, directed=False)
lcc_subgraph = networkit_util.get_lcc_subgraph(net)
logging.info('plot hop...')
paras = dict(net=lcc_subgraph, label=label, outpath=outpath)
networkit_plot.plot_hop_dist(**paras)
logging.info('')
logging.info('estimate average shortest path')
sample_num = 100
logging.info('sample size: {0}'.format(sample_num))
asp = networkit_util.get_average_shortest_path_appro(lcc_subgraph, sample_num=sample_num)
logging.info('estimated average shortest path: {0}'.format(asp))
```

4. calculate exact clustering coefficient

  net = networkit_util.readnetwork(filepath, directed=False)
  calculate_clustering_coefficient.get_exact_clustering_coefficient(net, label, outpath)

5. calculate betweeness centrality

  net = networkit_util.readnetwork(filepath, directed=False)
  calculate_betweeness.get_betweeness(net, label, outpath)

6. centrality analysis

  pagerank_filename = outpath + 'toy-pagerank-falseid-value'
  map_filename = outpath + 'directed-toy-map-node-ids'
  top_k_list = centrality_analysis.get_top_k_id(pagerank_filename, map_filename, 3)
  print(top_k_list)
  index_filename = './input/index'
  top_k_name = centrality_analysis.get_top_k_name(top_k_list, index_filename)
  print(top_k_name)
  centrality_analysis.power_law_analysis(pagerank_filename, 'pagerank', label, outpath)