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

• URL: http://arxiv.org/abs/2106.02926v3
• Date: Tue, 6 Feb 2024 13:38:40 GMT
• Title: IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology
• Authors: Cong Tran, Won-Yong Shin, Andreas Spitz
• Abstract summary: We propose a solution to influence (IM) in networks with unknown topology by retrieving information from queries and node metadata. 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 neural Siamese 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.
• Score: 13.704584231053675
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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