TY - GEN
T1 - Parameterized dynamic cluster editing
AU - Luo, Junjie
AU - Molter, Hendrik
AU - Nichterlein, André
AU - Niedermeier, Rolf
N1 - Publisher Copyright:
© Junjie Luo, Hendrik Molter, André Nichterlein, and Rolf Niedermeier.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - We introduce a dynamic version of the NP-hard Cluster Editing problem. The essential point here is to take into account dynamically evolving input graphs: Having a cluster graph (that is, a disjoint union of cliques) that represents a solution for a first input graph, can we cost-efficiently transform it into a “similar” cluster graph that is a solution for a second (“subsequent”) input graph? This model is motivated by several application scenarios, including incremental clustering, the search for compromise clusterings, or also local search in graph-based data clustering. We thoroughly study six problem variants (edge editing, edge deletion, edge insertion; each combined with two distance measures between cluster graphs). We obtain both fixed-parameter tractability as well as parameterized hardness results, thus (except for two open questions) providing a fairly complete picture of the parameterized computational complexity landscape under the perhaps two most natural parameterizations: the distance of the new “similar” cluster graph to (i) the second input graph and to (ii) the input cluster graph.
AB - We introduce a dynamic version of the NP-hard Cluster Editing problem. The essential point here is to take into account dynamically evolving input graphs: Having a cluster graph (that is, a disjoint union of cliques) that represents a solution for a first input graph, can we cost-efficiently transform it into a “similar” cluster graph that is a solution for a second (“subsequent”) input graph? This model is motivated by several application scenarios, including incremental clustering, the search for compromise clusterings, or also local search in graph-based data clustering. We thoroughly study six problem variants (edge editing, edge deletion, edge insertion; each combined with two distance measures between cluster graphs). We obtain both fixed-parameter tractability as well as parameterized hardness results, thus (except for two open questions) providing a fairly complete picture of the parameterized computational complexity landscape under the perhaps two most natural parameterizations: the distance of the new “similar” cluster graph to (i) the second input graph and to (ii) the input cluster graph.
KW - Compromise clustering
KW - Fixed-parameter tractability
KW - Goal-oriented clustering
KW - Graph-based data clustering
KW - NP-hard problems
KW - Parameterized hardness
UR - http://www.scopus.com/inward/record.url?scp=85079500663&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.FSTTCS.2018.46
DO - 10.4230/LIPIcs.FSTTCS.2018.46
M3 - Conference contribution
AN - SCOPUS:85079500663
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 38th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2018
A2 - Ganguly, Sumit
A2 - Pandya, Paritosh
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 38th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2018
Y2 - 11 December 2018 through 13 December 2018
ER -