GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization

TL;DR

This paper introduces GraMeR, a graph meta reinforcement learning framework that efficiently solves multi-objective influence maximization by addressing scalability, generalizability, and computational challenges in large networks.

Contribution

It formulates influence maximization as a Markov decision process and employs meta-learning with double Q-learning for scalable, generalizable seed node identification.

Findings

GraMeR is significantly faster than traditional methods.

It effectively handles large graphs with high accuracy.

The approach generalizes well across different network types.

Abstract

Influence maximization (IM) is a combinatorial problem of identifying a subset of nodes called the seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, the uses are limited due to the computational complexity of current algorithms. Recently, learning heuristics for IM have been explored to ease the computational burden. However, there are serious limitations in current approaches such as: (1) IM formulations only consider influence via spread and ignore self activation; (2) scalability to large graphs; (3) generalizability across graph families; (4) low computational efficiency with a large running time to identify seed sets for every test network. In this work, we address each of these limitations through a unique approach that involves (1) formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations; (2) employing double Q learning to estimate seed nodes; (3) ensuring scalability via sub-graph based representations; and (4) incorporating generalizability via meta-learning across graph families. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. The results indicate that GraMeR is multiple orders faster and generic than conventional approaches.