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Searching for polarization in signed graphs: a local spectral approach, WebConf 2020

See the paper (Arxiv version)

Software dependency

install python packages

Make sure you have conda installed.

Then run conda env create -f environment.yml to create the virtual environment.

Activate it by conda activate polar

Install database (optional)

We use postgres to store results of experiments that are 1) repeated many times and 2) relatively time-consuming to run. For example, results from seeding on real-world graphs are stored in database.

You don't need database if you only call the API in Python (core.py). However, if you want to reproduce the results in the paper, you need this.

testing

run pytest test*.py (you might see very few errors e.g., usually 1 from test_core.py due to numerical instability)

API usage

a typical example of calling functions in Python is:

import networkx as nx
from core import query_graph_using_sparse_linear_solver, sweep_on_x_fast
# read the graph
g = nx.read_gpickle('{path_of_graph}')

# compute the optimal x vector
x, obj_val = query_graph_using_sparse_linear_solver(g, [seeds1, seeds2], kappa=0.9, verbose=0, ub=g.graph['lambda1'])

# sweep on x to find C1 and C2
C1, C2, C, best_t, best_sbr, ts, sbr_list = sweep_on_x_fast(g, x, top_k=100)

print('community 1', C1)
print('community 2', C2)

Command line usage

run queries on graphs

currently, two query modes are supported:

  • query a single seed: run a batch of single-seed queries on graph: query_single_seed_in_batch.py
    • save commands that query single seed (for parallel computing): python3 print_query_single_seed_commands.py {graph} > cmds/{graph}.txt
  • query a seed pair (one node from each polarized side): run a batch of seed-pair queries on graph: `query_seed_pair_in_batch.py
    • save commands that query seed pairs: python3 print_query_seed_pair_commands.py {graph} > cmds/{graph}_pairs.txt

Note that the above two commands requires postgres being installed.

exporting results from database

use export_single_seed_result_from_db.py|export_pair_result_from_db.py

The output is in pandas.DataFrame format.

useful scripts

  • augment_result.py: augment our result by various graph statistics

pre/post-processing scripts for FOCG, KDD 2016

  • prepare_data_for_matlab.py: convert graph to Matlab format for FOCG to use
  • augment_focg_result.py: augment FOCG result by various graph statistics

scalability evaluation

  • run scalability_evaluation.py

Reproducing the figures/tables in the submission

data pre-processing

  • run preprocess_graph.py (remember to change the variable graph)
  • or you can use the processed ones under graphs/{graph}.pkl

Figure 1: the motivation plot

run intro-plot.ipynb

Table 2: graph statistics

run graph_stat_table.ipynb

Figure 4: synthetic graph experiment

run the following (it takes ~1.5 hours on a 8-core machine in total):

  • effect of noise parameter: run_experiment_effect_of_eta.py
  • effect of number of outlier nods: run_experiment_effect_of_outlier_size.py
  • effect of number of seed: run_experiment_effect_of_seed_size.py

then, make the plot using experiment_on_synthetic_graphs.ipynb

Figure 5: real graph experiment

You need to have postgres installed in order to save the results.

run PolarSeeds

Do the following for all graphs (word, bitcoin, epinions, etc):

  • run python3 print_query_seed_pair_commands.py {graph_name} > {cmd_list}.txt to get the list of execution commands
    • {cmd_list}.txt will contain the list of commands to run to get the result
  • run python3 export_pair_result_from_db.py to export the data (remember to change the graph variable in the script)
  • run python3 augment_pair_result.py {graph_name} to add evaluation metric values

run FOCG

  • before runnin FOCG, preprocess the graphs so they're Matlab-compatible: run prepare_data_for_matlab.py (remember to update te graph variable)
  • check this repository and run the file DemoRun.m in Matlab
    • make sure the input graphs from previou step are in the right paths
    • copy the output .mat file under outputs/focg-{graph_name}.mat
  • run python3 augment_focg_result.py {graph_name} to add evaluation metric values

make the plot

run FOCG-vs-PolarSeeds.ipynb to make the plot

Figure 6: case studies

  • (a) and (b): run case-study-overlapping-community.ipynb
  • (c): run case-study-distrust-radiation.ipynb

Jupyter notebooks along the way

the following notebooks are records of the thought process and how the project has evolved:

  • signed-laplacian-eigen-value.ipynb: demo on what the bottom-most eigen vector looks like on a toy graph (to understand better signed spectral theory in general)
  • proof-of-concept.ipynb: the very early one that demos how this method works for small toy graphs and some investigation on the effect of kappa
  • experiment_on_synthetic_graphs.ipynb: effect of different parameters on synetheic graphs
  • tuning-kappa.ipynb: trying to understand better the effect of kappa on synthetic graphs
  • binary-search-on-alpha.ipynb: binary search on alpha plus conjugate gradient method to solve the program
  • fast_sweeping.ipynb: efficient way to sweep on x (reduces time cost by orders of magnitudes)
  • case-study-on-word-graph.ipynb: manual checking the result on word graph + some visualization
  • why-constraint-not-tight.ipynb: for some nodes typically with small degrees, alpha tends very close to lambda_1, making the constraint not tight
  • explore-seed-pair-query-result.ipynb: checking query result on real graphs (some statistics and viz)
  • explore-fog-result-on-real-graphs.ipynb: checking query result by FOCG, KDD 2018 on real graphs
  • FOCG-vs-PolarSeeds.ipynb: comparing FOCG, KDD 2018 with our method
  • dig-out-more-communities-on-word-graph.ipynb: find out more polarized communities on "word" graph
  • case-study-overlapping-community: demo on overlapping communities in "word" graph
  • case-study-distrust-radiation.ipynb: case study of distrust radiation
  • intro-plot.ipynb: plots of the motivational example

Misc

notes on sbatch (Aalto Triton ulitity script)

  • edit sbatch_query_single_seed_in_batch.sh and make sure to update the following:
    • --array=1-{n}, where n is the number of commands to run (use wc -l {cmds_path.txt}) to get that number
    • graph="{graph_name}": set the graph name accordingly
    • in addition, number of cpus, memory requirement, max running time can be set
  • submit the job by `sbatch sbatch_run_queries_in_batch.sh
  • the same applies to the other query mode, corresponding to file sbatch_query_pairs_in_batch.sh

To cite this paper

@inproceedings{xiao2020searching,
  title={Searching for polarization in signed graphs: a local spectral approach},
  author={Xiao, Han and Ordozgoiti, Bruno and Gionis, Aristides},
  booktitle={Proceedings of The Web Conference 2020},
  pages={362--372},
  year={2020}
}