TY - GEN
T1 - Distributed detection of cliques in dynamic networks
AU - Bonne, Matthias
AU - Censor-Hillel, Keren
N1 - Publisher Copyright: © Matthias Bonne and Keren Censor-Hillel; licensed under Creative Commons License CC-BY
PY - 2019/7/1
Y1 - 2019/7/1
N2 - This paper provides an in-depth study of the fundamental problems of finding small subgraphs in distributed dynamic networks. While some problems are trivially easy to handle, such as detecting a triangle that emerges after an edge insertion, we show that, perhaps somewhat surprisingly, other problems exhibit a wide range of complexities in terms of the trade-offs between their round and bandwidth complexities. In the case of triangles, which are only affected by the topology of the immediate neighborhood, some end results are: The bandwidth complexity of 1-round dynamic triangle detection or listing is Θ(1). The bandwidth complexity of 1-round dynamic triangle membership listing is Θ(1) for node/edge deletions, Θ(n1/2) for edge insertions, and Θ(n) for node insertions. The bandwidth complexity of 1-round dynamic triangle membership detection is Θ(1) for node/edge deletions, O(log n) for edge insertions, and Θ(n) for node insertions. Most of our upper and lower bounds are tight. Additionally, we provide almost always tight upper and lower bounds for larger cliques.
AB - This paper provides an in-depth study of the fundamental problems of finding small subgraphs in distributed dynamic networks. While some problems are trivially easy to handle, such as detecting a triangle that emerges after an edge insertion, we show that, perhaps somewhat surprisingly, other problems exhibit a wide range of complexities in terms of the trade-offs between their round and bandwidth complexities. In the case of triangles, which are only affected by the topology of the immediate neighborhood, some end results are: The bandwidth complexity of 1-round dynamic triangle detection or listing is Θ(1). The bandwidth complexity of 1-round dynamic triangle membership listing is Θ(1) for node/edge deletions, Θ(n1/2) for edge insertions, and Θ(n) for node insertions. The bandwidth complexity of 1-round dynamic triangle membership detection is Θ(1) for node/edge deletions, O(log n) for edge insertions, and Θ(n) for node insertions. Most of our upper and lower bounds are tight. Additionally, we provide almost always tight upper and lower bounds for larger cliques.
KW - And phrases distributed computing
KW - Dynamic graphs
KW - Subgraph detection
UR - http://www.scopus.com/inward/record.url?scp=85069216200&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.ICALP.2019.132
DO - 10.4230/LIPIcs.ICALP.2019.132
M3 - منشور من مؤتمر
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 46th International Colloquium on Automata, Languages, and Programming, ICALP 2019
A2 - Baier, Christel
A2 - Chatzigiannakis, Ioannis
A2 - Flocchini, Paola
A2 - Leonardi, Stefano
T2 - 46th International Colloquium on Automata, Languages, and Programming, ICALP 2019
Y2 - 9 July 2019 through 12 July 2019
ER -