Fast and Error-Adaptive Influence Maximization based on Count-Distinct Sketches

TL;DR

This paper introduces a fast, error-adaptive influence maximization algorithm using Count-Distinct sketches and hash-based sampling, significantly improving speed and seed set quality over existing methods.

Contribution

It presents a novel influence maximization approach that combines error-adaptive sketch rebuilding with efficient diffusion simulation, achieving high speed and accuracy.

Findings

Up to 119x faster than state-of-the-art algorithms.

Produces seed sets with 3%-12% better influence scores.

Maintains high-quality influence maximization with reduced computational effort.

Abstract

Influence maximization (IM) is the problem of finding a seed vertex set that maximizes the expected number of vertices influenced under a given diffusion model. Due to the NP-Hardness of finding an optimal seed set, approximation algorithms are frequently used for IM. In this work, we describe a fast, error-adaptive approach that leverages Count-Distinct sketches and hash-based fused sampling. To estimate the number of influenced vertices throughout a diffusion, we use per-vertex Flajolet-Martin sketches where each sketch corresponds to a sampled subgraph. To efficiently simulate the diffusions, the reach-set cardinalities of a single vertex are stored in memory in a consecutive fashion. This allows the proposed algorithm to estimate the number of influenced vertices in a single step for simulations at once. For a faster IM kernel, we rebuild the sketches in parallel only after observing estimation errors above a given threshold. Our experimental results show that the proposed algorithm yields high-quality seed sets while being up to 119x faster than a state-of-the-art approximation algorithm. In addition, it is up to 62x faster than a sketch-based approach while producing seed sets with 3%-12% better influence scores