Densest Subhypergraph: Negative Supermodular Functions and Strongly Localized Methods

TL;DR

This paper introduces new algorithms for localized densest subgraph discovery in hypergraphs, extending previous work, and provides theoretical insights into when strongly-local algorithms are feasible, with applications in social media analysis.

Contribution

It generalizes the anchored densest subgraph problem to hypergraphs with a locality parameter, and proves conditions for strongly-local algorithms, also offering new algorithms for global dense subgraph problems.

Findings

Provided a strongly-local algorithm for certain locality parameters.

Proved impossibility of strongly-local algorithms below a threshold.

Developed the first strongly polynomial algorithm for densest supermodular set problem.

Abstract

Dense subgraph discovery is a fundamental primitive in graph and hypergraph analysis which among other applications has been used for real-time story detection on social media and improving access to data stores of social networking systems. We present several contributions for localized densest subgraph discovery, which seeks dense subgraphs located nearby given seed sets of nodes. We first introduce a generalization of a recent $\textit{anchored densest subgraph}$ problem, extending this previous objective to hypergraphs and also adding a tunable locality parameter that controls the extent to which the output set overlaps with seed nodes. Our primary technical contribution is to prove when it is possible to obtain a strongly-local algorithm for solving this problem, meaning that the runtime depends only on the size of the input set. We provide a strongly-local algorithm that applies whenever the locality parameter is not too small, and show via counterexample why strongly-local algorithms are impossible below a certain threshold. Along the way to proving our results for localized densest subgraph discovery, we also provide several advances in solving global dense subgraph discovery objectives. This includes the first strongly polynomial time algorithm for the densest supermodular set problem and a flow-based exact algorithm for a heavy and dense subgraph discovery problem in graphs with arbitrary node weights. We demonstrate our algorithms on several web-based data analysis tasks.