RESUMEN
Selecting a set of initial users from a social network in order to maximize the envisaged number of influenced users is known as influence maximization (IM). Researchers have achieved significant advancements in the theoretical design and performance gain of several classical approaches, but these advances are almost reaching their pinnacle. Learning-based IM approaches have emerged recently with a higher generalization to unknown graphs than conventional methods. The development of learning-based IM methods is still constrained by a number of fundamental hardships, including (1) solving the objective function efficiently, (2) struggling to characterize the diverse underlying diffusion patterns, and (3) adapting the solution to different node-centrality-constrained IM variants. To address the aforementioned issues, we design a novel framework DeepIM for generatively characterizing the latent representation of seed sets, as well as learning the diversified information diffusion pattern in a data-driven and end-to-end way. Subsequently, we design a novel objective function to infer optimal seed sets under flexible node-centrality-based budget constraints. Extensive analyses are conducted over both synthetic and real-world datasets to demonstrate the overall performance of DeepIM.
RESUMEN
In recent years, analyzing the explanation for the prediction of Graph Neural Networks (GNNs) has attracted increasing attention. Despite this progress, most existing methods do not adequately consider the inherent uncertainties stemming from the randomness of model parameters and graph data, which may lead to overconfidence and misguiding explanations. However, it is challenging for most of GNN explanation methods to quantify these uncertainties since they obtain the prediction explanation in a post-hoc and model-agnostic manner without considering the randomness of graph data and model parameters. To address the above problems, this paper proposes a novel uncertainty quantification framework for GNN explanations. For mitigating the randomness of graph data in the explanation, our framework accounts for two distinct data uncertainties, allowing for a direct assessment of the uncertainty in GNN explanations. For mitigating the randomness of learned model parameters, our method learns the parameter distribution directly from the data, obviating the need for assumptions about specific distributions. Moreover, the explanation uncertainty within model parameters is also quantified based on the learned parameter distributions. This holistic approach can integrate with any post-hoc GNN explanation methods. Empirical results from our study show that our proposed method sets a new standard for GNN explanation performance across diverse real-world graph benchmarks.