On some fine-grained questions in algorithms and complexity
VV Williams - Proceedings of the international congress of …, 2018 - World Scientific
In recent years, a new “fine-grained” theory of computational hardness has been developed,
based on “fine-grained reductions” that focus on exact running times for problems …
based on “fine-grained reductions” that focus on exact running times for problems …
What graph neural networks cannot learn: depth vs width
A Loukas - arXiv preprint arXiv:1907.03199, 2019 - arxiv.org
This paper studies the expressive power of graph neural networks falling within the
message-passing framework (GNNmp). Two results are presented. First, GNNmp are shown …
message-passing framework (GNNmp). Two results are presented. First, GNNmp are shown …
Recent advances in multi-pass graph streaming lower bounds
S Assadi - ACM SIGACT News, 2023 - dl.acm.org
Recent Advances in Multi-Pass Graph Streaming Lower Bounds Page 1 Recent Advances in
Multi-Pass Graph Streaming Lower Bounds Sepehr Assadi Cheriton School of Computer …
Multi-Pass Graph Streaming Lower Bounds Sepehr Assadi Cheriton School of Computer …
Universally-optimal distributed algorithms for known topologies
Many distributed optimization algorithms achieve existentially-optimal running times,
meaning that there exists some pathological worst-case topology on which no algorithm can …
meaning that there exists some pathological worst-case topology on which no algorithm can …
Fast approximate shortest paths in the congested clique
We design fast deterministic algorithms for distance computation in the CONGESTED
CLIQUE model. Our key contributions include: A (2+ ε)-approximation for all-pairs shortest …
CLIQUE model. Our key contributions include: A (2+ ε)-approximation for all-pairs shortest …
What's wrong with deep learning in tree search for combinatorial optimization
Combinatorial optimization lies at the core of many real-world problems. Especially since the
rise of graph neural networks (GNNs), the deep learning community has been developing …
rise of graph neural networks (GNNs), the deep learning community has been developing …
Adversarially robust coloring for graph streams
A Chakrabarti, P Ghosh, M Stoeckl - arXiv preprint arXiv:2109.11130, 2021 - arxiv.org
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 …
with high probability even when the stream updates are chosen by an adversary who may …
Distributed exact weighted all-pairs shortest paths in near-linear time
A Bernstein, D Nanongkai - Proceedings of the 51st Annual ACM …, 2019 - dl.acm.org
In the distributed all-pairs shortest paths problem (APSP), every node in the weighted
undirected distributed network (the CONGEST model) needs to know the distance from …
undirected distributed network (the CONGEST model) needs to know the distance from …
Brooks' theorem in graph streams: a single-pass semi-streaming algorithm for∆-coloring
Every graph with maximum degree Δ can be colored with (Δ+ 1) colors using a simple
greedy algorithm. Remarkably, recent work has shown that one can find such a coloring …
greedy algorithm. Remarkably, recent work has shown that one can find such a coloring …
Hardness of distributed optimization
This paper studies lower bounds for fundamental optimization problems in the CONGEST
model. We show that solving problems exactly in this model can be a hard task, by providing …
model. We show that solving problems exactly in this model can be a hard task, by providing …