IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology

TL;DR

IM-META is a novel approach for influence maximization in networks with unknown topology, leveraging node metadata and iterative querying to efficiently identify influential nodes with limited network information.

Contribution

It introduces a new influence maximization framework that combines metadata analysis, edge inference, and strategic node querying to operate effectively in unknown network topologies.

Findings

IM-META accelerates network exploration with fewer queries.

It outperforms benchmark methods in influence spread.

The method is robust and scalable across datasets.

Abstract

Since the structure of complex networks is often unknown, we may identify the most influential seed nodes by exploring only a part of the underlying network, given a small budget for node queries. We propose IM-META, a solution to influence maximization (IM) in networks with unknown topology by retrieving information from queries and node metadata. Since using such metadata is not without risk due to the noisy nature of metadata and uncertainties in connectivity inference, we formulate a new IM problem that aims to find both seed nodes and queried nodes. In IM-META, we develop an effective method that iteratively performs three steps: 1) we learn the relationship between collected metadata and edges via a Siamese neural network, 2) we select a number of inferred confident edges to construct a reinforced graph, and 3) we identify the next node to query by maximizing the inferred influence spread using our topology-aware ranking strategy. Through experimental evaluation of IM-META on four real-world datasets, we demonstrate a) the speed of network exploration via node queries, b) the effectiveness of each module, c) the superiority over benchmark methods, d) the robustness to more difficult settings, e) the hyperparameter sensitivity, and f) the scalability.