13 research outputs found
Streaming beyond sketching for Maximum Directed Cut
We give an -space single-pass -approximation
streaming algorithm for estimating the maximum directed cut size
() in a directed graph on vertices. This improves over
an -space approximation algorithm due to Chou,
Golovnev, Velusamy (FOCS 2020), which was known to be optimal for
-space algorithms.
is a special case of a constraint satisfaction problem
(CSP). In this broader context, our work gives the first CSP for which
algorithms with space can provably outperform
-space algorithms on general instances. Previously, this was shown
in the restricted case of bounded-degree graphs in a previous work of the
authors (SODA 2023). Prior to that work, the only algorithms for any CSP were
based on generalizations of the -space algorithm for
, and were in particular so-called "sketching" algorithms.
In this work, we demonstrate that more sophisticated streaming algorithms can
outperform these algorithms even on general instances.
Our algorithm constructs a "snapshot" of the graph and then applies a result
of Feige and Jozeph (Algorithmica, 2015) to approximately estimate the
value from this snapshot. Constructing this snapshot is
easy for bounded-degree graphs and the main contribution of our work is to
construct this snapshot in the general setting. This involves some delicate
sampling methods as well as a host of "continuity" results on the
behaviour in graphs.Comment: 57 pages, 2 figure
Recommended from our members
Computation over the Noisy Broadcast Channel with Malicious Parties
We study the n-party noisy broadcast channel with a constant fraction of malicious parties. Specifically, we assume that each non-malicious party holds an input bit, and communicates with the others in order to learn the input bits of all non-malicious parties. In each communication round, one of the parties broadcasts a bit to all other parties, and the bit received by each party is flipped with a fixed constant probability (independently for each recipient). How many rounds are needed?
Assuming there are no malicious parties, Gallager gave an (n log log n)-round protocol for the above problem, which was later shown to be optimal. This protocol, however, inherently breaks down in the presence of malicious parties.
We present a novel n ⋅ ̃(√{log n})-round protocol, that solves this problem even when almost half of the parties are malicious. Our protocol uses a new type of error correcting code, which we call a locality sensitive code and which may be of independent interest. Roughly speaking, these codes map "close" messages to "close" codewords, while messages that are not close are mapped to codewords that are very far apart.
We view our result as a first step towards a theory of property preserving interactive coding, i.e., interactive codes that preserve useful properties of the protocol being encoded. In our case, the naive protocol over the noiseless broadcast channel, where all the parties broadcast their input bit and output all the bits received, works even in the presence of malicious parties. Our simulation of this protocol, unlike Gallager’s, preserves this property of the original protocol
Protecting Single-Hop Radio Networks from Message Drops
Single-hop radio networks (SHRN) are a well studied abstraction of communication over a wireless channel. In this model, in every round, each of the n participating parties may decide to broadcast a message to all the others, potentially causing collisions. We consider the SHRN model in the presence of stochastic message drops (i.e., erasures), where in every round, the message received by each party is erased (replaced by ?) with some small constant probability, independently.
Our main result is a constant rate coding scheme, allowing one to run protocols designed to work over the (noiseless) SHRN model over the SHRN model with erasures. Our scheme converts any protocol ? of length at most exponential in n over the SHRN model to a protocol ?\u27 that is resilient to constant fraction of erasures and has length linear in the length of ?.
We mention that for the special case where the protocol ? is non-adaptive, i.e., the order of communication is fixed in advance, such a scheme was known. Nevertheless, adaptivity is widely used and is known to hugely boost the power of wireless channels, which makes handling the general case of adaptive protocols ? both important and more challenging. Indeed, to the best of our knowledge, our result is the first constant rate scheme that converts adaptive protocols to noise resilient ones in any multi-party model
Streaming complexity of CSPs with randomly ordered constraints
We initiate a study of the streaming complexity of constraint satisfaction
problems (CSPs) when the constraints arrive in a random order. We show that
there exists a CSP, namely , for which random ordering
makes a provable difference. Whereas a approximation of
requires space with adversarial ordering,
we show that with random ordering of constraints there exists a
-approximation algorithm that only needs space. We also give
new algorithms for in variants of the adversarial ordering
setting. Specifically, we give a two-pass space
-approximation algorithm for general graphs and a single-pass
space -approximation algorithm for bounded degree
graphs.
On the negative side, we prove that CSPs where the satisfying assignments of
the constraints support a one-wise independent distribution require
-space for any non-trivial approximation, even when the
constraints are randomly ordered. This was previously known only for
adversarially ordered constraints. Extending the results to randomly ordered
constraints requires switching the hard instances from a union of random
matchings to simple Erd\"os-Renyi random (hyper)graphs and extending tools that
can perform Fourier analysis on such instances.
The only CSP to have been considered previously with random ordering is
where the ordering is not known to change the
approximability. Specifically it is known to be as hard to approximate with
random ordering as with adversarial ordering, for space
algorithms. Our results show a richer variety of possibilities and motivate
further study of CSPs with randomly ordered constraints
Near-Optimal Two-Pass Streaming Algorithm for Sampling Random Walks over Directed Graphs
For a directed graph G with n vertices and a start vertex u_start, we wish to (approximately) sample an L-step random walk over G starting from u_start with minimum space using an algorithm that only makes few passes over the edges of the graph. This problem found many applications, for instance, in approximating the PageRank of a webpage. If only a single pass is allowed, the space complexity of this problem was shown to be ??(n ? L). Prior to our work, a better space complexity was only known with O?(?L) passes.
We essentially settle the space complexity of this random walk simulation problem for two-pass streaming algorithms, showing that it is ??(n ? ?L), by giving almost matching upper and lower bounds. Our lower bound argument extends to every constant number of passes p, and shows that any p-pass algorithm for this problem uses ??(n ? L^{1/p}) space. In addition, we show a similar ??(n ? ?L) bound on the space complexity of any algorithm (with any number of passes) for the related problem of sampling an L-step random walk from every vertex in the graph