A hallmark of graph neural networks is their ability to distinguish the isomorphism class of
their inputs. This study derives hardness results for the classification variant of graph …

In the distributed subgraph detection problem, we are given a fixed subgraph H, and the
network must decide whether the network graph contains a copy of H or not. Subgraph …

In this paper we give sublinear-time distributed algorithms in the CONGEST CONGEST
model for finding or listing cliques and even-length cycles. We show for the first time that all …

We present improved distributed algorithms for triangle detection and its variants in the
CONGEST model. We show that Triangle Detection, Counting, and Enumeration can be …

This is a survey of the exciting recent progress made in understanding the complexity of
distributed subgraph finding problems. It overviews the results and techniques for assorted …

The problem of detecting network structures plays a central role in distributed computing.
One of the fundamental problems studied in this area is to determine whether for a given …

We show an Õ (np/(p+ 2))-round algorithm in the CONGEST model for listing of Kp (a clique
with p nodes), for all p= 4, p≥ 6. For p= 5, we show an Õ (n 3/4)-round algorithm. For p= 4 …

The importance of classifying connections in large graphs has been the motivation for a rich
line of work on distributed subgraph finding that has led to exciting recent breakthroughs. A …

In this paper we explore fundamental problems in randomized communication complexity
such as computing SetIntersection on sets of size k and EqualityTesting between vectors of …

Much progress has recently been made in understanding the complexity landscape of
subgraph finding problems in the CONGEST model of distributed computing. However, so …