Directory Structure:
================================================================================ data folder:
- cascade-time-int-all.txt
- casc-user-id.txt
- followers.p
================================================================================ STD folders:
- top users at peak time [eg. top_at_peak_temporal_std_1_window_0]
- top users at any time [eg. top_any_temporal_std_1_window_0]
- top users in random order [eg. top_random_std_1_window_0]
================================================================================ output folder:
- Average gain exposure (structural and temporal)
- Average relative gain (structural and temporal)
- Single seed simulation (structural and temporal)
- borda with k-truss (ranked list [k-truss, page rank, MCDWE])
- s-t ranked lists
- t-s ranked lists
================================================================================ ranked_lists folder:
- contains ranked top users
================================================================================ graphs folder:
- contains all the graphs
================================================================================ src folder:
- find_k_truss_decomposition_on_active_network.py
- finds active network
- implemented k-truss on active network
- output in active_k_truss_score file in output folder
- find_k_truss_decomposition_on_global_network.py
- implemented k-truss on global network
- output in score_k_truss_from_dictionary file in output folder
- find_mcdwe_score.py
- implemented mcdwe method on global network
- output in mcdwe_score.txt file in output folder (node - value pair)
- find_page-rank_and_degree-centrality.py
- implemented page rank and degree centrality on global network
- output in page_rank_cent_directed.txt and degree_cent_score.txt file in output folder (node - value pair)
- find_t-s.py
- Finds only temporal and only structural users
- output in output folder (s-t_[number of top users] and t-s_[number of top users] files)
- metric_average_gain_exposure.py
- metric implemented from the main.pdf paper
- three functions (temporal, structural,structural_difference)
- for temporal users, files from [STD 1, STD 2, STD 3, STD 4] folders are used (ranked lists)
- temporal output in temp_expo.txt file in output folder.
- for structural all structural ranked lists ranked_lists folder is used.
- same for only structural and only temporal users.
- metric_average_relative_gain.py
- metric implemented from the main.pdf paper
- three functions (temporal, structural,structural_difference)
- for temporal users, files from [STD 1, STD 2, STD 3, STD 4] folders are used (ranked lists)
- temporal output in temp_expo.txt file in output folder.
- for structural all structural ranked lists ranked_lists folder is used.
- same for only structural and only temporal users.
- metric_multi_seed.py
- metric implemented from the main.pdf paper
- two functions (temporal, structural)
- for temporal users, files from [STD 1, STD 2, STD 3, STD 4] folders are used (ranked lists)
- temporal output in temp_expo.txt file in output folder.
- for structural all structural ranked lists ranked_lists folder is used.
- metric_single_seed_simulation.py
- metric implemented from the main.pdf paper
- three functions (temporal, structural,structural_difference)
- for temporal users, files from [STD 1, STD 2, STD 3, STD 4] folders are used (ranked lists)
- temporal output in temp_expo.txt file in output folder.
- for structural all structural ranked lists ranked_lists folder is used.
- same for only structural and only temporal users.
- plot_graph.py
- all the values are calculated from the above files.
- x - axis (top users [50,100,150,200,250,300])
- y - axis (metric values)
- temporal.py
- temporal users
- output in STD [sta dev num] folders