Aduri Pavan

Counting and sampling triangles from a graph stream

This paper presents a new space-efficient algorithm for counting and sampling triangles--and more generally, constant-sized cliques--in a massive graph whose edges arrive as a stream. Compared to prior work, our algorithm yields significant improvements in the space and time complexity for these fundamental problems. Our algorithm is simple to implement and has very good practical performance on large graphs.

Separation of NP-Completeness Notions

We use hypotheses of structural complexity theory to separate various NP-completeness notions. In particular, we introduce an hypothesis from which we describe a set in NP that is

On the hardness of permanent

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Extracting kolmogorov complexity with applications to dimension zero-one laws

{\leq}^{\rm P}_{\rm T}

Parallel triangle counting in massive streaming graphs

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Hardness hypotheses, derandomization, and circuit complexity

-complete but not

Range-Efficient Counting of Distinct Elements in a Massive Data Stream

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Range-efficient computation of F/sub 0/ over massive data streams

{\leq}^{\rm P}_{tt}

An O(n½+?)-Space and Polynomial-Time Algorithm for Directed Planar Reachability

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New Time-Space Upperbounds for Directed Reachability in High-genus and H-minor-free Graphs.

-complete. We provide fairly thorough analyses of the hypotheses that we introduce.