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Record W4411141667 · doi:10.1145/3725228

An Improved Fully Dynamic Algorithm for Counting 4-Cycles in General Graphs Using Fast Matrix Multiplication

2025· article· en· W4411141667 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the ACM on Management of Data · 2025
Typearticle
Languageen
FieldComputer Science
TopicGraph Theory and Algorithms
Canadian institutionsUniversity of Waterloo
FundersUniversitas Brawijaya
KeywordsMatrix multiplicationMultiplication (music)AlgorithmComputer scienceMatrix (chemical analysis)Multiplication algorithmArithmeticMathematicsParallel computingCombinatoricsPhysicsMaterials science

Abstract

fetched live from OpenAlex

We study subgraph counting over fully dynamic graphs, which undergo edge insertions and deletions. Counting subgraphs is a fundamental problem in graph theory with numerous applications across various fields, including database theory, social network analysis, and computational biology. In database theory, we can use dynamic subgraph counting algorithms on layered graphs to maintain the sizes of joins of databases that undergo updates. Specifically, the problem of finding the number of elements in a cyclic join of size k is equivalent to counting the number of k-cycles in k-layered graphs. For example, let R, S, and T be relations that have schemas (A, B), (B, C), and (C, A) respectively. Then the size of the join of R with S with T is given by the number of triangles in the corresponding layered graph where there is a layer for each attribute, the vertices are the attribute values and the edges represent the tuples of attribute values in the relations. Maintaining the number of triangles in fully dynamic graphs is very well studied and has an upper bound of O(√m) for the update time [KNN+20]. There is also a conditional lower bound of Ω(m 1/2-γ ) for any constant γ>0, for the update time [HKNS15] under the Online Matrix-Vector (OMv) conjecture implying that O(√m) is the ''right answer' for the update time of counting triangles. More recently, [HHH22] studied the problem of maintaining the number of 4-cycles in fully dynamic graphs and designed an algorithm with O(m 2/3 ) update time which is a natural generalization of the approach for counting triangles. They also studied the problem of counting 4-cliques showing that the folklore upper bound of O(m) for the update time is tight under the static combinatorial 4-clique conjecture by giving a lower bound of Ω(m 1-γ ) for any γ>0. Thus, it seems natural that O(m 2/3 ) might be the correct answer for the complexity of the update time for counting 4-cycles. In this work, we present an improved algorithm for maintaining the number of 4-cycles in fully dynamic graphs. Our algorithm achieves a worst-case update time of O(m 2/3-ε ) for some constant ε>0. We also show that the problem of counting 4-cycles is equivalent in layered graphs and general graphs. Our approach crucially uses fast matrix multiplication and leverages recent developments therein to get an improved runtime. Using the current best value of the matrix multiplication exponent ω=2.371339 we get ε=0.009811 and if we assume the best possible exponent i.e. ω=2 then we get ε=1/24. There is also a lower bound of Ω(m 1/2-γ ) for any constant γ>0, for the update time [HKNS15,HHH22], so there is still a big gap between the best-known upper and lower bounds. The key message of our paper is demonstrating that O(m 2/3 ) is not the correct answer for the complexity of the update time.

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0060.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.322
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it