28 citations · 49 across the 7 of their papers we have counts for
7 papers
Single-Pass Estimation of the Clustering Coefficient Distribution in Graph Streams
Cristian Boldrin, C. Seshadhri
Triangle counting is one of the most fundamental problems in network analysis. Given the massive sizes of real-world graphs, there is a long history of small-space streaming algori…
RNG: Flat Datacenter Networks at Scale
Giacomo Bernardi, Ratul Mahajan, C. Seshadhri +8
We design and deploy in production the first flat datacenter networks. Our design, called RNG, is based on quasi-random graphs. While the cost and fault-tolerance benefits of such…
A note on approximating the average degree of bounded arboricity graphs
Talya Eden, C. Seshadhri
Estimating the average degree of graph is a classic problem in sublinear graph algorithm. Eden, Ron, and Seshadhri (ICALP 2017, SIDMA 2019) gave a simple algorithm for this problem…
A Sublinear Algorithm for Approximate Shortest Paths in Large Networks
Sabyasachi Basu, Nadia Kōshima, Talya Eden +2
Computing distances and finding shortest paths in massive real-world networks is a fundamental algorithmic task in network analysis. There are two main approaches to solving this t…
FAST-PPR: Scaling Personalized PageRank Estimation for Large Graphs
Peter Lofgren, Siddhartha Banerjee, Ashish Goel +1
We propose a new algorithm, FAST-PPR, for estimating personalized PageRank: given start node and target node in a directed graph, and given a threshold , FAST-PPR estima…
Decompositions of Triangle-Dense Graphs
Rishi Gupta, Tim Roughgarden, C. Seshadhri
High triangle density -- the graph property stating that a constant fraction of two-hop paths belong to a triangle -- is a common signature of social networks. This paper studies t…
Self-Improving Algorithms
Nir Ailon, Bernard Chazelle, Kenneth L. Clarkson +3
We investigate ways in which an algorithm can improve its expected performance by fine-tuning itself automatically with respect to an unknown input distribution D. We assume here t…