Deep Graph Representation Learning for Influence Maximization
Introduction
Influence maximization (IM) aims to identify a set of initial users in a social network that maximizes the expected number of users influenced by their actions. This problem has significant applications in viral marketing, information dissemination, and social network analysis. Traditional approaches to IM have achieved considerable progress, but their performance gains are approaching a limit. Recently, learning-based IM methods have emerged, demonstrating stronger generalization capabilities on unknown graphs compared to traditional methods. However, the development of learning-based IM methods faces several fundamental challenges.
Challenges in Learning-Based Influence Maximization
Several obstacles hinder the advancement of learning-based IM methods:
Difficulty in Solving the Objective Function: Efficiently optimizing the complex objective function in IM remains a significant challenge. The search space for selecting the optimal seed set grows exponentially with the network size, making it computationally expensive to find the best solution.
Difficulty in Characterizing Diversified Underlying Diffusion Patterns: Accurately capturing the intricate and varied ways in which information spreads through a social network is challenging. Diffusion patterns can be influenced by various factors, including network topology, user behavior, and the nature of the information being disseminated.
Difficulty in Adapting the Solution Under Various Node-Centrality-Constrained IM Variants: Adapting the IM solution to accommodate different constraints, such as node centrality, poses a challenge. Node centrality measures the importance of a node within a network, and incorporating these measures as constraints can lead to more realistic and practical IM solutions.
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DeepIM: A Novel Framework for Influence Maximization
To address these challenges, a novel framework called DeepIM has been developed. DeepIM generatively characterizes the latent representation of seed sets and learns the diversified information diffusion pattern in a data-driven and end-to-end manner. This approach enables DeepIM to overcome the limitations of traditional and existing learning-based IM methods.
Latent Representation of Seed Sets
DeepIM characterizes the latent representation of seed sets, capturing the essential features and relationships within the set. This representation allows the model to understand the characteristics of influential users and their potential impact on the network.
Data-Driven Learning of Diffusion Patterns
DeepIM learns the diversified information diffusion pattern using a data-driven approach. By analyzing historical diffusion data, the model can identify the underlying patterns and dynamics of information spread. This enables DeepIM to accurately predict the influence of seed sets and optimize the selection process.
End-to-End Learning
DeepIM is trained in an end-to-end manner, meaning that the entire model is optimized directly for the IM task. This eliminates the need for manual feature engineering or intermediate steps, allowing the model to learn the optimal representation and diffusion patterns directly from the data.
Objective Function for Optimal Seed Set Inference
DeepIM employs a novel objective function to infer optimal seed sets under flexible node-centrality-based budget constraints. This objective function considers both the expected influence of the seed set and the constraints imposed by node centrality measures. By optimizing this objective function, DeepIM can identify seed sets that maximize influence while adhering to the specified constraints.
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Performance Evaluation
Extensive analyses have been conducted on both synthetic and real-world datasets to evaluate the overall performance of DeepIM. The results demonstrate that DeepIM outperforms traditional and existing learning-based IM methods in terms of influence spread and generalization ability.
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tags: #deep #graph #representation #learning #influence #maximization
