RedQueen: An Online Algorithm for Smart Broadcasting on Social Networks
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redqueen
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README.md
RedQueen-demo.ipynb
opt_broadcast.ipynb
setup.py

README.md

RedQueen

This is a repository containing code for the paper:

A. Zarezade, U. Upadhyay, H. R. Raibee, M. Gomez-Rodriguez. RedQueen: An Online Algorithm for Smart Broadcasting in Social Networks. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM), 2017.

Pre-requisites

This code depends on the following packages:

  1. decorated_options: Installation instructions are at musically-ut/decorated_options or pip install decorated_options.
  2. broadcast_ref package (i.e. Karimi et.al.'s method, which is the used as a baseline). Follow the instructions at Networks-Learning/broadcast_ref.

Code structure

  • opt_models.py contains models for various broadcasters and baselines:

    • Poisson (random posting)
    • Hawkes (bursty posting)
    • PiecewiseConst (different rates at different times)
    • RealData (emulates behavior of a real user in our dataset)
    • RedQueen (our proposed algorithm)
  • utils.py contains common utility functions for metric calculation and plotting.

  • opt_runs.py contains functions to execute the simulations.

  • real_data_gen.py and read_real_data.py files deal with conversion of real Twitter data to and from formats helpful for our models.

Execution

The code execution is detailed in the included IPython notebooks.

As an example, say if we have the following structure in our network of broadcasters (i.e. sources) and followers (i.e. sinks), with Source 1 being the broadcaster we control:

   Source 1**      Source 2      Source 3
    +   +             +                 +
    |   |             |                 |
    |   +----------------------------+  |
    |                 |              |  |
    |    +------------+-----------+  |  |
    |    |            |           |  |  |
   +v----v-+      +---v---+      +v--v--v+
   |       |      |       |      |       |
   |       |      |       |      |       |
   |       |      |       |      |       |
   |       |      |       |      |       |
   |       |      |       |      |       |
   |       |      |       |      |       |
   +-------+      +-------+      +-------+
    Sink 1          Sink 2         Sink 3

This will be represented in the following way:

simOpts = SimOpts(
   src_id = 1,
   end_time = 100, # When the simulations stop
   s = { 1: 1.0, 3: 1.0 }, # Weights of followers of source 1
   q=1.0, # Control parameter for RedQueen
   sink_ids = [1, 2, 3],
   other_sources = [
      ('Poisson2', { 'src_id': 2,
                    'seed': 42,
                    'rate': 10
                  }),
      ('Hawkes',  { 'src_id': 3,
                    'seed': 43,
                    'l_0': 10,
                    'alpha': 1.0,
                    'beta': 10.0
                  })
   ],
   edge_list=[(1, 1), (1, 3), 
              (2, 1), (2, 2), (2, 3),
              (3, 3)]
);

These SimOpts objects are immutable and can be used to create multiple simulations.

Then to run the simulation, we need to create a simulation Manager by instantiating Source 1 to be a kind of broadcaster, or by removing it altogether.

manager = simOpts.create_manager_with_opt(seed=101)
# or 
manager = simOpts.create_manager_for_wall()

Finally, run the simulation by calling .run_dynamic:

manager.run_dynamic()

Finally, the list of events can be extracted for further analysis:

df = manager.state.get_dataframe()

The file utils.py contains some functions which can assist in calculation of certain metrics:

import redqueen.utils as U
perf_1 = U.time_in_top_k(df=df, K=1, sim_opts=simOpts)
perf_2 = U.average_rank(df=df, sim_opts=simOpts)