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Link to original content: https://doi.org/10.4230/LIPIcs.SEA.2023.11
CompDP: A Framework for Simultaneous Subgraph Counting Under Connectivity Constraints
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CompDP: A Framework for Simultaneous Subgraph Counting Under Connectivity Constraints

Authors Kengo Nakamura , Masaaki Nishino , Norihito Yasuda, Shin-ichi Minato

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Author Details

Kengo Nakamura
  • NTT Communication Science Laboratories, Kyoto, Japan
  • Graduate School of Informatics, Kyoto University, Japan
Masaaki Nishino
  • NTT Communication Science Laboratories, Kyoto, Japan
Norihito Yasuda
  • NTT Communication Science Laboratories, Kyoto, Japan
Shin-ichi Minato
  • Graduate School of Informatics, Kyoto University, Japan

Funding

This work was supported by JSPS KAKENHI Grant Number JP20H05963 and JST CREST Grant Number JPMJCR22D3.

Cite AsGet BibTex

Kengo Nakamura, Masaaki Nishino, Norihito Yasuda, and Shin-ichi Minato. CompDP: A Framework for Simultaneous Subgraph Counting Under Connectivity Constraints. In 21st International Symposium on Experimental Algorithms (SEA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 265, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.SEA.2023.11

Abstract

The subgraph counting problem computes the number of subgraphs of a given graph that satisfy some constraints. Among various constraints imposed on a graph, those regarding the connectivity of vertices, such as "these two vertices must be connected," have great importance since they are indispensable for determining various graph substructures, e.g., paths, Steiner trees, and rooted spanning forests. In this view, the subgraph counting problem under connectivity constraints is also important because counting such substructures often corresponds to measuring the importance of a vertex in network infrastructures. However, we must solve the subgraph counting problems multiple times to compute such an importance measure for every vertex. Conventionally, they are solved separately by constructing decision diagrams such as BDD and ZDD for each problem. However, even solving a single subgraph counting is a computationally hard task, preventing us from solving it multiple times in a reasonable time. In this paper, we propose a dynamic programming framework that simultaneously counts subgraphs for every vertex by focusing on similar connectivity constraints. Experimental results show that the proposed method solved multiple subgraph counting problems about 10-20 times faster than the existing approach for many problem settings.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Graph theory
Keywords
  • Subgraph counting
  • Connectivity
  • Zero-suppressed Binary Decision Diagram

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