Clustering on kk-edge-colored graphs

Eric Angel (1) , Evripidis Bampis (2) , Dimitris Paparas (3) , Emmanouil Pountourakis (4) , Vassilis Zissimopoulos (5)

1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
2 RO - Recherche Opérationnelle
3 UBC-Computer Science - Computer Science Department
4 EECS department
5 Communication Networks Laboratory [Athens]

We study the Max kk-colored clustering problem, where given an edge-colored graph with kk colors, we seek to color the vertices of the graph so as to find a clustering of the vertices maximizing the number (or the weight) of matched edges, i.e. the edges having the same color as their extremities. We show that the cardinality problem is NP-hard even for edge-colored bipartite graphs with a chromatic degree equal to two and k≥3k≥3. Our main result is a constant approximation algorithm for the weighted version of the Max kk-colored clustering problem which is based on a rounding of a natural linear programming relaxation. For graphs with chromatic degree equal to two we improve this ratio by exploiting the relation of our problem with the Max 2-and problem. We also present a reduction to the maximum-weight independent set (IS) problem in bipartite graphs which leads to a polynomial time algorithm for the case of two colors.

Mots clés: Clustering; Approximation algorithms; Coloring

Domaines: Algorithme et structure de données [cs.DS]

Eric Angel, Evripidis Bampis, Dimitris Paparas, Emmanouil Pountourakis, Vassilis Zissimopoulos. Clustering on kk-edge-colored graphs. Discrete Applied Mathematics, 2016, 211, pp.15-22. ⟨10.1016/j.dam.2016.04.017⟩. ⟨hal-01366446⟩