Distributed Local Multi-Aggregation and Centrality Approximation

TL;DR

This paper introduces a flexible distributed framework for local graph aggregation and analysis in the CONGEST model, enabling efficient approximation of centrality measures and routing costs even with overlapping neighborhoods.

Contribution

It presents a novel algorithm that performs multiple local aggregations simultaneously in $O(|S|+k)$ rounds, applicable to complex graph analysis beyond simple aggregation trees.

Findings

Efficient approximation of centrality measures.

Approximation of minimum routing cost trees.

Algorithm runs in $O(|S|+k)$ rounds regardless of neighborhood overlaps.

Abstract

We study local aggregation and graph analysis in distributed environments using the message passing model. We provide a flexible framework, where each of the nodes in a set $S$--which is a subset of all nodes in the network--can perform a large range of common aggregation functions in its $k$-neighborhood. We study this problem in the CONGEST model, where in each synchronous round, every node can transmit a different (but short) message to each of its neighbors. While the $k$-neighborhoods of nodes in $S$ might overlap and aggregation could cause congestion in this model, we present an algorithm that needs time $O(|S|+k)$ even when each of the nodes in $S$ performs a different aggregation on its $k$-neighborhood. The framework is not restricted to aggregation-trees such that it can be used for more advanced graph analysis. We demonstrate this by providing efficient approximations of centrality measures and approximation of minimum routing cost trees.