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**Published in:** LIPIcs, Volume 287, 15th Innovations in Theoretical Computer Science Conference (ITCS 2024)

We present novel lower bounds in the Merlin-Arthur (MA) communication model and the related annotated streaming or stream verification model. The MA communication model extends the classical communication model by introducing an all-powerful but untrusted player, Merlin, who knows the inputs of the usual players, Alice and Bob, and attempts to convince them about the output. We focus on the online MA (OMA) model where Alice and Merlin each send a single message to Bob, who needs to catch Merlin if he is dishonest and announce the correct output otherwise. Most known functions have OMA protocols with total communication significantly smaller than what would be needed without Merlin. In this work, we introduce the notion of non-trivial-OMA complexity of a function. This is the minimum total communication required when we restrict ourselves to only non-trivial protocols where Alice sends Bob fewer bits than what she would have sent without Merlin. We exhibit the first explicit functions that have this complexity superlinear - even exponential - in their classical one-way complexity: this means the trivial protocol, where Merlin communicates nothing and Alice and Bob compute the function on their own, is exponentially better than any non-trivial protocol in terms of total communication. These OMA lower bounds also translate to the annotated streaming model, the MA analogue of single-pass data streaming. We show large separations between the classical streaming complexity and the non-trivial annotated streaming complexity (for the analogous notion in this setting) of fundamental problems such as counting distinct items, as well as of graph problems such as connectivity and k-connectivity in a certain edge update model called the support graph turnstile model that we introduce here.

Prantar Ghosh and Vihan Shah. New Lower Bounds in Merlin-Arthur Communication and Graph Streaming Verification. In 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp. 53:1-53:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{ghosh_et_al:LIPIcs.ITCS.2024.53, author = {Ghosh, Prantar and Shah, Vihan}, title = {{New Lower Bounds in Merlin-Arthur Communication and Graph Streaming Verification}}, booktitle = {15th Innovations in Theoretical Computer Science Conference (ITCS 2024)}, pages = {53:1--53:22}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-309-6}, ISSN = {1868-8969}, year = {2024}, volume = {287}, editor = {Guruswami, Venkatesan}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2024.53}, URN = {urn:nbn:de:0030-drops-195815}, doi = {10.4230/LIPIcs.ITCS.2024.53}, annote = {Keywords: Graph Algorithms, Streaming, Communication Complexity, Stream Verification, Merlin-Arthur Communication, Lower Bounds} }

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**Published in:** LIPIcs, Volume 215, 13th Innovations in Theoretical Computer Science Conference (ITCS 2022)

A streaming algorithm is considered to be adversarially robust if it provides correct outputs with high probability even when the stream updates are chosen by an adversary who may observe and react to the past outputs of the algorithm. We grow the burgeoning body of work on such algorithms in a new direction by studying robust algorithms for the problem of maintaining a valid vertex coloring of an n-vertex graph given as a stream of edges. Following standard practice, we focus on graphs with maximum degree at most Δ and aim for colorings using a small number f(Δ) of colors.
A recent breakthrough (Assadi, Chen, and Khanna; SODA 2019) shows that in the standard, non-robust, streaming setting, (Δ+1)-colorings can be obtained while using only Õ(n) space. Here, we prove that an adversarially robust algorithm running under a similar space bound must spend almost Ω(Δ²) colors and that robust O(Δ)-coloring requires a linear amount of space, namely Ω(nΔ). We in fact obtain a more general lower bound, trading off the space usage against the number of colors used. From a complexity-theoretic standpoint, these lower bounds provide (i) the first significant separation between adversarially robust algorithms and ordinary randomized algorithms for a natural problem on insertion-only streams and (ii) the first significant separation between randomized and deterministic coloring algorithms for graph streams, since deterministic streaming algorithms are automatically robust.
We complement our lower bounds with a suite of positive results, giving adversarially robust coloring algorithms using sublinear space. In particular, we can maintain an O(Δ²)-coloring using Õ(n √Δ) space and an O(Δ³)-coloring using Õ(n) space.

Amit Chakrabarti, Prantar Ghosh, and Manuel Stoeckl. Adversarially Robust Coloring for Graph Streams. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 215, pp. 37:1-37:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{chakrabarti_et_al:LIPIcs.ITCS.2022.37, author = {Chakrabarti, Amit and Ghosh, Prantar and Stoeckl, Manuel}, title = {{Adversarially Robust Coloring for Graph Streams}}, booktitle = {13th Innovations in Theoretical Computer Science Conference (ITCS 2022)}, pages = {37:1--37:23}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-217-4}, ISSN = {1868-8969}, year = {2022}, volume = {215}, editor = {Braverman, Mark}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2022.37}, URN = {urn:nbn:de:0030-drops-156332}, doi = {10.4230/LIPIcs.ITCS.2022.37}, annote = {Keywords: Data streaming, graph algorithms, graph coloring, lower bounds, online algorithms} }

Document

**Published in:** LIPIcs, Volume 182, 40th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2020)

We study the general problem of computing frequency-based functions, i.e., the sum of any given function of data stream frequencies. Special cases include fundamental data stream problems such as computing the number of distinct elements (F₀), frequency moments (F_k), and heavy-hitters. It can also be applied to calculate the maximum frequency of an element (F_{∞}).
Given that exact computation of most of these special cases provably do not admit any sublinear space algorithm, a natural approach is to consider them in an enhanced data streaming model, where we have a computationally unbounded but untrusted prover that can send proofs or help messages to ease the computation. Think of a memory-restricted client delegating the computation to a powerful cloud service. The client does not blindly trust the cloud, and with its limited memory, it wants to verify the proof that the cloud sends. Chakrabarti et al. (ICALP '09) introduced this model as the annotated data streaming model and showed that multiple problems including exact computation of frequency-based functions - that have no sublinear algorithms in basic streaming - do have algorithms, also called schemes, in the annotated streaming model with both space and proof-length sublinear in the input size.
We give a general scheme for computing any frequency-based function with both space usage and proof-size of O(n^{2/3}log n) bits, where n is the size of the universe. This improves upon the best known bound of O(n^{2/3}log^{4/3} n) given by the seminal paper of Chakrabarti et al. and as a result, also improves upon the best known bounds for the important special cases of computing F₀ and F_{∞}. We emphasize that while being quantitatively better, our scheme is also qualitatively better in the sense that it is simpler than the previously best scheme that uses intricate data structures and elaborate subroutines. Our scheme uses a simple technique tailored for this model: the verifier solves the problem partially by running an algorithm known to be helpful for it in the basic (sans prover) streaming model and then takes the prover’s help to solve the remaining part.

Prantar Ghosh. New Verification Schemes for Frequency-Based Functions on Data Streams. In 40th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 182, pp. 22:1-22:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{ghosh:LIPIcs.FSTTCS.2020.22, author = {Ghosh, Prantar}, title = {{New Verification Schemes for Frequency-Based Functions on Data Streams}}, booktitle = {40th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2020)}, pages = {22:1--22:15}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-174-0}, ISSN = {1868-8969}, year = {2020}, volume = {182}, editor = {Saxena, Nitin and Simon, Sunil}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2020.22}, URN = {urn:nbn:de:0030-drops-132631}, doi = {10.4230/LIPIcs.FSTTCS.2020.22}, annote = {Keywords: data streams, interactive proofs, Arthur-Merlin} }

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RANDOM

**Published in:** LIPIcs, Volume 176, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020)

We study graph computations in an enhanced data streaming setting, where a space-bounded client reading the edge stream of a massive graph may delegate some of its work to a cloud service. We seek algorithms that allow the client to verify a purported proof sent by the cloud service that the work done in the cloud is correct. A line of work starting with Chakrabarti et al. (ICALP 2009) has provided such algorithms, which we call schemes, for several statistical and graph-theoretic problems, many of which exhibit a tradeoff between the length of the proof and the space used by the streaming verifier.
This work designs new schemes for a number of basic graph problems - including triangle counting, maximum matching, topological sorting, and single-source shortest paths - where past work had either failed to obtain smooth tradeoffs between these two key complexity measures or only obtained suboptimal tradeoffs. Our key innovation is having the verifier compute certain nonlinear sketches of the input stream, leading to either new or improved tradeoffs. In many cases, our schemes in fact provide optimal tradeoffs up to logarithmic factors.
Specifically, for most graph problems that we study, it is known that the product of the verifier’s space cost v and the proof length h must be at least Ω(n²) for n-vertex graphs. However, matching upper bounds are only known for a handful of settings of h and v on the curve h ⋅ v = Θ̃(n²). For example, for counting triangles and maximum matching, schemes with costs lying on this curve are only known for (h = Õ(n²), v = Õ(1)), (h = Õ(n), v = Õ(n)), and the trivial (h = Õ(1), v = Õ(n²)). A major message of this work is that by exploiting nonlinear sketches, a significant "portion" of costs on the tradeoff curve h ⋅ v = n² can be achieved.

Amit Chakrabarti, Prantar Ghosh, and Justin Thaler. Streaming Verification for Graph Problems: Optimal Tradeoffs and Nonlinear Sketches. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 176, pp. 22:1-22:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{chakrabarti_et_al:LIPIcs.APPROX/RANDOM.2020.22, author = {Chakrabarti, Amit and Ghosh, Prantar and Thaler, Justin}, title = {{Streaming Verification for Graph Problems: Optimal Tradeoffs and Nonlinear Sketches}}, booktitle = {Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020)}, pages = {22:1--22:23}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-164-1}, ISSN = {1868-8969}, year = {2020}, volume = {176}, editor = {Byrka, Jaros{\l}aw and Meka, Raghu}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2020.22}, URN = {urn:nbn:de:0030-drops-126258}, doi = {10.4230/LIPIcs.APPROX/RANDOM.2020.22}, annote = {Keywords: data streams, interactive proofs, Arthur-Merlin, graph algorithms} }

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Track A: Algorithms, Complexity and Games

**Published in:** LIPIcs, Volume 168, 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)

We study the problem of coloring a given graph using a small number of colors in several well-established models of computation for big data. These include the data streaming model, the general graph query model, the massively parallel communication (MPC) model, and the CONGESTED-CLIQUE and the LOCAL models of distributed computation. On the one hand, we give algorithms with sublinear complexity, for the appropriate notion of complexity in each of these models. Our algorithms color a graph G using κ(G)⋅(1+o(1)) colors, where κ(G) is the degeneracy of G: this parameter is closely related to the arboricity α(G). As a function of κ(G) alone, our results are close to best possible, since the optimal number of colors is κ(G)+1. For several classes of graphs, including real-world "big graphs," our results improve upon the number of colors used by the various (Δ(G)+1)-coloring algorithms known for these models, where Δ(G) is the maximum degree in G, since Δ(G) ⩾ κ(G) and can in fact be arbitrarily larger than κ(G).
On the other hand, we establish certain lower bounds indicating that sublinear algorithms probably cannot go much further. In particular, we prove that any randomized coloring algorithm that uses at most κ(G)+O(1) colors would require Ω(n²) storage in the one pass streaming model, and Ω(n²) many queries in the general graph query model, where n is the number of vertices in the graph. These lower bounds hold even when the value of κ(G) is known in advance; at the same time, our upper bounds do not require κ(G) to be given in advance.

Suman K. Bera, Amit Chakrabarti, and Prantar Ghosh. Graph Coloring via Degeneracy in Streaming and Other Space-Conscious Models. In 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 168, pp. 11:1-11:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{bera_et_al:LIPIcs.ICALP.2020.11, author = {Bera, Suman K. and Chakrabarti, Amit and Ghosh, Prantar}, title = {{Graph Coloring via Degeneracy in Streaming and Other Space-Conscious Models}}, booktitle = {47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)}, pages = {11:1--11:21}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-138-2}, ISSN = {1868-8969}, year = {2020}, volume = {168}, editor = {Czumaj, Artur and Dawar, Anuj and Merelli, Emanuela}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2020.11}, URN = {urn:nbn:de:0030-drops-124182}, doi = {10.4230/LIPIcs.ICALP.2020.11}, annote = {Keywords: Data streaming, Graph coloring, Sublinear algorithms, Massively parallel communication, Distributed algorithms} }

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RANDOM

**Published in:** LIPIcs, Volume 145, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)

We give new algorithms in the annotated data streaming setting - also known as verifiable data stream computation - for certain graph problems. This setting is meant to model outsourced computation, where a space-bounded verifier limited to sequential data access seeks to overcome its computational limitations by engaging a powerful prover, without needing to trust the prover. As is well established, several problems that admit no sublinear-space algorithms under traditional streaming do allow protocols using a sublinear amount of prover/verifier communication and sublinear-space verification. We give algorithms for many well-studied graph problems including triangle counting, its generalization to subgraph counting, maximum matching, problems about the existence (or not) of short paths, finding the shortest path between two vertices, and testing for an independent set. While some of these problems have been studied before, our results achieve new tradeoffs between space and communication costs that were hitherto unknown. In particular, two of our results disprove explicit conjectures of Thaler (ICALP, 2016) by giving triangle counting and maximum matching algorithms for n-vertex graphs, using o(n) space and o(n^2) communication.

Amit Chakrabarti and Prantar Ghosh. Streaming Verification of Graph Computations via Graph Structure. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 145, pp. 70:1-70:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{chakrabarti_et_al:LIPIcs.APPROX-RANDOM.2019.70, author = {Chakrabarti, Amit and Ghosh, Prantar}, title = {{Streaming Verification of Graph Computations via Graph Structure}}, booktitle = {Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)}, pages = {70:1--70:20}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-125-2}, ISSN = {1868-8969}, year = {2019}, volume = {145}, editor = {Achlioptas, Dimitris and V\'{e}gh, L\'{a}szl\'{o} A.}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2019.70}, URN = {urn:nbn:de:0030-drops-112856}, doi = {10.4230/LIPIcs.APPROX-RANDOM.2019.70}, annote = {Keywords: data streams, interactive proofs, Arthur-Merlin, graph algorithms} }

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