Dense Subgraphs on Dynamic Networks

TL;DR

This paper presents distributed algorithms for nodes in dynamic networks to identify dense subgraphs with approximation guarantees, using only knowledge of the network's dynamic diameter and operating under the CONGEST model.

Contribution

It introduces the first distributed algorithms that maintain dense subgraph awareness in dynamic networks with provable approximation ratios.

Findings

Algorithms achieve ($2 + psilon$)-approximation for densest subgraph.

Algorithms achieve ($3 + psilon$)-approximation for at-least-$k$-densest subgraph.

Algorithms run in $O(D\u2217 abla_{1+psilon} n)$ time, where $D$ is the dynamic diameter.

Abstract

In distributed networks, it is often useful for the nodes to be aware of dense subgraphs, e.g., such a dense subgraph could reveal dense subtructures in otherwise sparse graphs (e.g. the World Wide Web or social networks); these might reveal community clusters or dense regions for possibly maintaining good communication infrastructure. In this work, we address the problem of self-awareness of nodes in a dynamic network with regards to graph density, i.e., we give distributed algorithms for maintaining dense subgraphs that the member nodes are aware of. The only knowledge that the nodes need is that of the dynamic diameter $D$, i.e., the maximum number of rounds it takes for a message to traverse the dynamic network. For our work, we consider a model where the number of nodes are fixed, but a powerful adversary can add or remove a limited number of edges from the network at each time step. The communication is by broadcast only and follows the CONGEST model. Our algorithms are continuously executed on the network, and at any time (after some initialization) each node will be aware if it is part (or not) of a particular dense subgraph. We give algorithms that ($2 + \epsilon$)-approximate the densest subgraph and ($3 + \epsilon$)-approximate the at-least-$k$-densest subgraph (for a given parameter $k$). Our algorithms work for a wide range of parameter values and run in $O(D\log_{1+\epsilon} n)$ time. Further, a special case of our results also gives the first fully decentralized approximation algorithms for densest and at-least-$k$-densest subgraph problems for static distributed graphs.