Algorithms for comparing, aggregating, and clustering directed acyclis graphs (DAGs).
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extra
music_preferences
LICENSE
README.md
dag_aggregation_experiments.m
dag_clustering_experiments.m
dag_dist.m
generate_toy_dags.m
graph_k_means.m
optimize_pq.m
study_music_preferences.m
transitive_closure.m
update_center_empty_baseline.m
update_center_greedy.m
update_center_median.m
update_center_median_greedy.m
update_center_nondag.m

README.md

Comparing, aggregating, and clustering directed acyclis graphs (DAGs).

Code for paper:

Eric Malmi, Nikolaj Tatti, and Aristides Gionis, "Beyond rankings: comparing directed acyclic graphs". In Data Mining and Knowledge Discovery, 2015. (url)

Main files

  • dag_dist.m Distance measure for DAGs.
  • update_center_greedy.m Greedy algorithm for DAG aggregation.
  • update_center_median.m Median algorithm for DAG aggregation.
  • graph_k_mean.m K-Means approach for DAG clustering.