9 Search Results for "Wang, Yuyang"

Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2026.147
Local Computation Algorithms for (Minimum) Spanning Trees on Expander Graphs

Authors: Pan Peng and Yuyang Wang

Published in: LIPIcs, Volume 374, 53rd International Colloquium on Automata, Languages, and Programming (ICALP 2026)

Abstract
We study local computation algorithms (LCAs) for constructing spanning trees. In this setting, the goal is to determine locally, for each edge e ∈ E, whether it belongs to a spanning tree T of the input graph G, where T is defined implicitly by G and the randomness of the algorithm. It is known that sublinear-probe LCAs for spanning trees do not exist in general graphs, even for simple graph families. We identify a natural and well-studied class of graphs - expander graphs - that do admit sublinear-time LCAs for spanning trees. This is perhaps surprising, as previous work on expanders only succeeded in designing LCAs for sparse spanning subgraphs, rather than full spanning trees. We design an LCA with probe complexity O(√n ((log²n)/ϕ² + d)) for graphs with conductance at least ϕ and maximum degree at most d (not necessarily constant), which is nearly optimal when ϕ and d are constants, since Ω(√n) probes are necessary even for expanders. Next, we show that for the natural class of Erdős-Rényi graphs G(n, p) with np = n^δ for any constant δ > 0 (which are expanders with high probability), the √n lower bound can be bypassed. Specifically, we give an average-case LCA for such graphs with probe complexity Õ(√{n^{1 - δ}}). Finally, we extend our techniques to design LCAs for the minimum spanning tree (MST) problem on weighted expander graphs. Specifically, given a d-regular unweighted graph ̄{G} with sufficiently strong expansion, we consider the weighted graph G obtained by assigning to each edge an independent and uniform random weight from {1,…,W}, where W ≤ d/2 and W = o(log n). We show that there exists an LCA that is consistent with an exact MST of G, with probe complexity Õ(√nd²).
Cite as

Pan Peng and Yuyang Wang. Local Computation Algorithms for (Minimum) Spanning Trees on Expander Graphs. In 53rd International Colloquium on Automata, Languages, and Programming (ICALP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 374, pp. 147:1-147:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{peng_et_al:LIPIcs.ICALP.2026.147,
  author =	{Peng, Pan and Wang, Yuyang},
  title =	{{Local Computation Algorithms for (Minimum) Spanning Trees on Expander Graphs}},
  booktitle =	{53rd International Colloquium on Automata, Languages, and Programming (ICALP 2026)},
  pages =	{147:1--147:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-428-4},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{374},
  editor =	{Bhattacharya, Sayan and Nanongkai, Danupon and Benedikt, Michael and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2026.147},
  URN =		{urn:nbn:de:0030-drops-265361},
  doi =		{10.4230/LIPIcs.ICALP.2026.147},
  annote =	{Keywords: Local Computation Algorithms, (Minimum) Spanning Trees, Expander Graphs, Random Graphs}
}
Document
DOI: 10.4230/OASIcs.AEiC.2026.1
Model-Agnostic Uncertainty-Aware Semantic Segmentation with Conformal Risk Guarantees for Scene Understanding

Authors: Bakary Badjie, José Cecílio, Nils-Jonathan Friedrich, Norman Seyffer, Georg Jäger, and António Casimiro

Published in: OASIcs, Volume 143, 30th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2026)

Abstract
Accurate and reliable scene segmentation is a fundamental requirement for autonomous navigation systems operating in open and dynamic environments. As these systems increasingly rely on data-driven perception modules, their safety and operational robustness hinge on well-calibrated uncertainty estimates that can support explicit control of prediction errors through conformal calibration. Most existing uncertainty-aware segmentation approaches remain architecture-specific and are not evaluated under a common uncertainty-and-calibration protocol across distinct segmentation architectures and datasets. This work introduces a model-agnostic conformal segmentation pipeline that enables operationally meaningful, calibration-based error control in real-world deployments. The proposed framework treats segmentation networks as black boxes and operates on per-pixel class probabilities that are fine-tuned through evidential deep learning (EDL) to decompose aleatoric and epistemic uncertainties. We then apply pixel-wise, class-conditional split-conformal calibration to derive acceptance thresholds for user-defined target error rates. We instantiate the pipeline with DINOv2, Mask2Former, and SegFormer and evaluate it on a newly collected Lisbon street scene (LiSS) dataset; additional cross-dataset results on COCO, using a restricted set of safety-relevant classes, are reported in the appendix. Results show architecture- and class-dependent in-domain uncertainty-error alignment and indicate that dataset shift weakens uncertainty-based filtering and conformal risk control. This motivates continuous monitoring and recalibration as a practical requirement for trustworthy segmentation in safety-critical navigation.
Cite as

Bakary Badjie, José Cecílio, Nils-Jonathan Friedrich, Norman Seyffer, Georg Jäger, and António Casimiro. Model-Agnostic Uncertainty-Aware Semantic Segmentation with Conformal Risk Guarantees for Scene Understanding. In 30th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2026). Open Access Series in Informatics (OASIcs), Volume 143, pp. 1:1-1:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{badjie_et_al:OASIcs.AEiC.2026.1,
  author =	{Badjie, Bakary and Cec{\'\i}lio, Jos\'{e} and Friedrich, Nils-Jonathan and Seyffer, Norman and J\"{a}ger, Georg and Casimiro, Ant\'{o}nio},
  title =	{{Model-Agnostic Uncertainty-Aware Semantic Segmentation with Conformal Risk Guarantees for Scene Understanding}},
  booktitle =	{30th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2026)},
  pages =	{1:1--1:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-425-3},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{143},
  editor =	{Filieri, Antonio and Backeman, Peter},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AEiC.2026.1},
  URN =		{urn:nbn:de:0030-drops-259199},
  doi =		{10.4230/OASIcs.AEiC.2026.1},
  annote =	{Keywords: semantic segmentation, uncertainty quantification, evidential deep learning, conformal prediction, risk control, selective prediction}
}
Document
RANDOM
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2025.32
Quantum Property Testing in Sparse Directed Graphs

Authors: Simon Apers, Frédéric Magniez, Sayantan Sen, and Dániel Szabó

Published in: LIPIcs, Volume 353, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2025)

Abstract
We initiate the study of quantum property testing in sparse directed graphs, and more particularly in the unidirectional model, where the algorithm is allowed to query only the outgoing edges of a vertex. In the classical unidirectional model, the problem of testing k-star-freeness, and more generally k-source-subgraph-freeness, is almost maximally hard for large k. We prove that this problem has almost quadratic advantage in the quantum setting. Moreover, we show that this advantage is nearly tight, by showing a quantum lower bound using the method of dual polynomials on an intermediate problem for a new, property testing version of the k-collision problem that was not studied before. To illustrate that not all problems in graph property testing admit such a quantum speedup, we consider the problem of 3-colorability in the related undirected bounded-degree model, when graphs are now undirected. This problem is maximally hard to test classically, and we show that also quantumly it requires a linear number of queries.
Cite as

Simon Apers, Frédéric Magniez, Sayantan Sen, and Dániel Szabó. Quantum Property Testing in Sparse Directed Graphs. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 353, pp. 32:1-32:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{apers_et_al:LIPIcs.APPROX/RANDOM.2025.32,
  author =	{Apers, Simon and Magniez, Fr\'{e}d\'{e}ric and Sen, Sayantan and Szab\'{o}, D\'{a}niel},
  title =	{{Quantum Property Testing in Sparse Directed Graphs}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2025)},
  pages =	{32:1--32:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-397-3},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{353},
  editor =	{Ene, Alina and Chattopadhyay, Eshan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2025.32},
  URN =		{urn:nbn:de:0030-drops-243987},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2025.32},
  annote =	{Keywords: property testing, quantum computing, bounded-degree directed graphs, dual polynomial method, collision finding}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2025.51
Word Structures and Their Automatic Presentations

Authors: Xiaoyang Gong, Bakh Khoussainov, and Yuyang Zhuge

Published in: LIPIcs, Volume 345, 50th International Symposium on Mathematical Foundations of Computer Science (MFCS 2025)

Abstract
We study automatic presentations of the structures (ℕ; S), (ℕ; E_S), (ℕ; ≤), and their expansions by a unary predicate U. Here S is the successor function, E_S is the undirected version of S, and ≤ is the natural order. We call these structures word structures. Our goal is three-fold. First, we study the isomorphism problem for automatic word structures by focusing on the following three problems. The first problem asks to design an algorithm that, given an automatic structure A, decides if A is isomorphic to (ℕ; S). The second asks to design an algorithm that, given two automatic presentations of (ℕ; S, U₁) and (ℕ; S, U₂), where U₁ and U₂ are unary predicates, decides if these structures are isomorphic. The third problem investigates if there is an algorithm that, given two automatic presentations of (ℕ; ≤, U₁) and (ℕ; ≤, U₂), decides whether U₁ ∩ U₂ ≠ ∅. We show that these problems are undecidable. Next, we study intrinsic regularity of the function S in the structure Path_ω = (ℕ; E_S). We build an automatic presentation of Path_ω in which S is not regular. This implies that S is not intrinsically regular in Path_ω. For U ⊆ ℕ, let d_U be the function that computes the distances between the consecutive elements of U. We build automatic presentations of (ℕ; ≤, U) where d_U can realise logarithmic, radical, intermediate, and exponential functions.
Cite as

Xiaoyang Gong, Bakh Khoussainov, and Yuyang Zhuge. Word Structures and Their Automatic Presentations. In 50th International Symposium on Mathematical Foundations of Computer Science (MFCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 345, pp. 51:1-51:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{gong_et_al:LIPIcs.MFCS.2025.51,
  author =	{Gong, Xiaoyang and Khoussainov, Bakh and Zhuge, Yuyang},
  title =	{{Word Structures and Their Automatic Presentations}},
  booktitle =	{50th International Symposium on Mathematical Foundations of Computer Science (MFCS 2025)},
  pages =	{51:1--51:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-388-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{345},
  editor =	{Gawrychowski, Pawe{\l} and Mazowiecki, Filip and Skrzypczak, Micha{\l}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2025.51},
  URN =		{urn:nbn:de:0030-drops-241581},
  doi =		{10.4230/LIPIcs.MFCS.2025.51},
  annote =	{Keywords: Automatic structures, the isomorphism problem, decidability, undecidability, regular relations}
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.CP.2025.1
Privacy-Preserving SAT Solving (Invited Talk)

Authors: Ruzica Piskac

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
This is an extended abstract of the invited talk presented at the joint conferences "28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)" and "31st International Conference on Principles and Practice of Constraint Programming (CP 2025)". The talk is based on a series of three papers published previously, and it provides a unified overview of their key ideas and results.
Cite as

Ruzica Piskac. Privacy-Preserving SAT Solving (Invited Talk). In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 1:1-1:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{piskac:LIPIcs.CP.2025.1,
  author =	{Piskac, Ruzica},
  title =	{{Privacy-Preserving SAT Solving}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{1:1--1:2},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.1},
  URN =		{urn:nbn:de:0030-drops-238626},
  doi =		{10.4230/LIPIcs.CP.2025.1},
  annote =	{Keywords: SAT solving, Privacy-preserving reasoning, Zero-knowledge proofs, Propositional unsatisfiability}
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.SAT.2025.1
Privacy-Preserving SAT Solving (Invited Talk)

Authors: Ruzica Piskac

Published in: LIPIcs, Volume 341, 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)

Abstract
This is an extended abstract of the invited talk presented at the joint conferences "28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)" and "31st International Conference on Principles and Practice of Constraint Programming (CP 2025)". The talk is based on a series of three papers published previously, and it provides a unified overview of their key ideas and results.
Cite as

Ruzica Piskac. Privacy-Preserving SAT Solving (Invited Talk). In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 1:1-1:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{piskac:LIPIcs.SAT.2025.1,
  author =	{Piskac, Ruzica},
  title =	{{Privacy-Preserving SAT Solving}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{1:1--1:2},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-381-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{341},
  editor =	{Berg, Jeremias and Nordstr\"{o}m, Jakob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.1},
  URN =		{urn:nbn:de:0030-drops-237356},
  doi =		{10.4230/LIPIcs.SAT.2025.1},
  annote =	{Keywords: SAT solving, Privacy-preserving reasoning, Zero-knowledge proofs, Propositional unsatisfiability}
}
Document
Survey
DOI: 10.4230/TGDK.1.1.4
Knowledge Graph Embeddings: Open Challenges and Opportunities

Authors: Russa Biswas, Lucie-Aimée Kaffee, Michael Cochez, Stefania Dumbrava, Theis E. Jendal, Matteo Lissandrini, Vanessa Lopez, Eneldo Loza Mencía, Heiko Paulheim, Harald Sack, Edlira Kalemi Vakaj, and Gerard de Melo

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
While Knowledge Graphs (KGs) have long been used as valuable sources of structured knowledge, in recent years, KG embeddings have become a popular way of deriving numeric vector representations from them, for instance, to support knowledge graph completion and similarity search. This study surveys advances as well as open challenges and opportunities in this area. For instance, the most prominent embedding models focus primarily on structural information. However, there has been notable progress in incorporating further aspects, such as semantics, multi-modal, temporal, and multilingual features. Most embedding techniques are assessed using human-curated benchmark datasets for the task of link prediction, neglecting other important real-world KG applications. Many approaches assume a static knowledge graph and are unable to account for dynamic changes. Additionally, KG embeddings may encode data biases and lack interpretability. Overall, this study provides an overview of promising research avenues to learn improved KG embeddings that can address a more diverse range of use cases.
Cite as

Russa Biswas, Lucie-Aimée Kaffee, Michael Cochez, Stefania Dumbrava, Theis E. Jendal, Matteo Lissandrini, Vanessa Lopez, Eneldo Loza Mencía, Heiko Paulheim, Harald Sack, Edlira Kalemi Vakaj, and Gerard de Melo. Knowledge Graph Embeddings: Open Challenges and Opportunities. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 4:1-4:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{biswas_et_al:TGDK.1.1.4,
  author =	{Biswas, Russa and Kaffee, Lucie-Aim\'{e}e and Cochez, Michael and Dumbrava, Stefania and Jendal, Theis E. and Lissandrini, Matteo and Lopez, Vanessa and Menc{\'\i}a, Eneldo Loza and Paulheim, Heiko and Sack, Harald and Vakaj, Edlira Kalemi and de Melo, Gerard},
  title =	{{Knowledge Graph Embeddings: Open Challenges and Opportunities}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{4:1--4:32},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.4},
  URN =		{urn:nbn:de:0030-drops-194783},
  doi =		{10.4230/TGDK.1.1.4},
  annote =	{Keywords: Knowledge Graphs, KG embeddings, Link prediction, KG applications}
}
Document
Vision
DOI: 10.4230/TGDK.1.1.8
Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges

Authors: Claudia d'Amato, Louis Mahon, Pierre Monnin, and Giorgos Stamou

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
The graph model is nowadays largely adopted to model a wide range of knowledge and data, spanning from social networks to knowledge graphs (KGs), representing a successful paradigm of how symbolic and transparent AI can scale on the World Wide Web. However, due to their unprecedented volume, they are generally tackled by Machine Learning (ML) and mostly numeric based methods such as graph embedding models (KGE) and deep neural networks (DNNs). The latter methods have been proved lately very efficient, leading the current AI spring. In this vision paper, we introduce some of the main existing methods for combining KGs and ML, divided into two categories: those using ML to improve KGs, and those using KGs to improve results on ML tasks. From this introduction, we highlight research gaps and perspectives that we deem promising and currently under-explored for the involved research communities, spanning from KG support for LLM prompting, integration of KG semantics in ML models to symbol-based methods, interpretability of ML models, and the need for improved benchmark datasets. In our opinion, such perspectives are stepping stones in an ultimate view of KGs as central assets for neuro-symbolic and explainable AI.
Cite as

Claudia d'Amato, Louis Mahon, Pierre Monnin, and Giorgos Stamou. Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 8:1-8:35, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{damato_et_al:TGDK.1.1.8,
  author =	{d'Amato, Claudia and Mahon, Louis and Monnin, Pierre and Stamou, Giorgos},
  title =	{{Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{8:1--8:35},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.8},
  URN =		{urn:nbn:de:0030-drops-194824},
  doi =		{10.4230/TGDK.1.1.8},
  annote =	{Keywords: Graph-based Learning, Knowledge Graph Embeddings, Large Language Models, Explainable AI, Knowledge Graph Completion \& Curation}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2023.96
An Optimal Separation Between Two Property Testing Models for Bounded Degree Directed Graphs

Authors: Pan Peng and Yuyang Wang

Published in: LIPIcs, Volume 261, 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023)

Abstract
We revisit the relation between two fundamental property testing models for bounded-degree directed graphs: the bidirectional model in which the algorithms are allowed to query both the outgoing edges and incoming edges of a vertex, and the unidirectional model in which only queries to the outgoing edges are allowed. Czumaj, Peng and Sohler [STOC 2016] showed that for directed graphs with both maximum indegree and maximum outdegree upper bounded by d, any property that can be tested with query complexity O_{ε,d}(1) in the bidirectional model can be tested with n^{1-Ω_{ε,d}(1)} queries in the unidirectional model. In particular, {if the proximity parameter ε approaches 0, then the query complexity of the transformed tester in the unidirectional model approaches n}. It was left open if this transformation can be further improved or there exists any property that exhibits such an extreme separation. We prove that testing subgraph-freeness in which the subgraph contains k source components, requires Ω(n^{1-1/k}) queries in the unidirectional model. This directly gives the first explicit properties that exhibit an O_{ε,d}(1) vs Ω(n^{1-f(ε,d)}) separation of the query complexities between the bidirectional model and unidirectional model, where f(ε,d) is a function that approaches 0 as ε approaches 0. Furthermore, our lower bound also resolves a conjecture by Hellweg and Sohler [ESA 2012] on the query complexity of testing k-star-freeness.
Cite as

Pan Peng and Yuyang Wang. An Optimal Separation Between Two Property Testing Models for Bounded Degree Directed Graphs. In 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 261, pp. 96:1-96:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{peng_et_al:LIPIcs.ICALP.2023.96,
  author =	{Peng, Pan and Wang, Yuyang},
  title =	{{An Optimal Separation Between Two Property Testing Models for Bounded Degree Directed Graphs}},
  booktitle =	{50th International Colloquium on Automata, Languages, and Programming (ICALP 2023)},
  pages =	{96:1--96:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-278-5},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{261},
  editor =	{Etessami, Kousha and Feige, Uriel and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2023.96},
  URN =		{urn:nbn:de:0030-drops-181480},
  doi =		{10.4230/LIPIcs.ICALP.2023.96},
  annote =	{Keywords: Graph property testing, Directed graphs, Lower bound, Subgraph-freeness}
}
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