First-Order Model-Checking in Random Graphs and Complex Networks
Abstract
Complex networks are everywhere. They appear for example in the form of biological networks, social networks, or computer networks and have been studied extensively. Efficient algorithms to solve problems on complex networks play a central role in today's society. Algorithmic meta-theorems show that many problems can be solved efficiently. Since logic is a powerful tool to model problems, it has been used to obtain very general meta-theorems. In this work, we consider all problems definable in first-order logic and analyze which properties of complex networks allow them to be solved efficiently. The mathematical tool to describe complex networks are random graph models. We define a property of random graph models called $α$-power-law-boundedness. Roughly speaking, a random graph is $α$-power-law-bounded if it does not admit strong clustering and its degree sequence is bounded by a power-law distribution with exponent at least $α$ (i.e. the fraction of vertices with degree $k$ is roughly $O(k^{-α})$). We solve the first-order model-checking problem (parameterized by the length of the formula) in almost linear FPT time on random graph models satisfying this property with $α\ge 3$. This means in particular that one can solve every problem expressible in first-order logic in almost linear expected time on these random graph models. This includes for example preferential attachment graphs, Chung-Lu graphs, configuration graphs, and sparse Erdős-Rényi graphs. Our results match known hardness results and generalize previous tractability results on this topic.
Authors 3
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Affiliation as printed
Department of Computer Science, RWTH Aachen University, Germany
RWTH Aachen University
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Affiliation as printed
Department of Computer Science, RWTH Aachen University, Germany
RWTH Aachen University
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Affiliation as printed
Department of Computer Science, RWTH Aachen University, Germany
RWTH Aachen University
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References 63
-
W2769133055details pending0citations
-
W1572272766details pending0citations
-
W2905110430details pending0citations
-
W2053991811details pending0citations
-
W2798588639details pending0citations
-
W2034198367details pending0citations
-
W2085536555details pending0citations
-
W2104812688details pending0citations
-
W2066585576details pending0citations
-
W2028357390details pending0citations
-
W891461details pending0citations