Tensor Convolutional Network for Higher-Order Interaction Prediction in Sparse Tensors

• URL: http://arxiv.org/abs/2503.11786v1
• Date: Fri, 14 Mar 2025 18:22:20 GMT
• Title: Tensor Convolutional Network for Higher-Order Interaction Prediction in Sparse Tensors
• Authors: Jun-Gi Jang, Jingrui He, Andrew Margenot, Hanghang Tong,
• Abstract summary: We propose TCN, an accurate and compatible tensor convolutional network that integrates seamlessly with TF methods for predicting top-k interactions.<n>We show that TCN integrated with a TF method outperforms competitors, including TF methods and a hyperedge prediction method.
• Score: 74.31355755781343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many real-world data, such as recommendation data and temporal graphs, can be represented as incomplete sparse tensors where most entries are unobserved. For such sparse tensors, identifying the top-k higher-order interactions that are most likely to occur among unobserved ones is crucial. Tensor factorization (TF) has gained significant attention in various tensor-based applications, serving as an effective method for finding these top-k potential interactions. However, existing TF methods primarily focus on effectively fusing latent vectors of entities, which limits their expressiveness. Since most entities in sparse tensors have only a few interactions, their latent representations are often insufficiently trained. In this paper, we propose TCN, an accurate and compatible tensor convolutional network that integrates seamlessly with existing TF methods for predicting higher-order interactions. We design a highly effective encoder to generate expressive latent vectors of entities. To achieve this, we propose to (1) construct a graph structure derived from a sparse tensor and (2) develop a relation-aware encoder, TCN, that learns latent representations of entities by leveraging the graph structure. Since TCN complements traditional TF methods, we seamlessly integrate TCN with existing TF methods, enhancing the performance of predicting top-k interactions. Extensive experiments show that TCN integrated with a TF method outperforms competitors, including TF methods and a hyperedge prediction method. Moreover, TCN is broadly compatible with various TF methods and GNNs (Graph Neural Networks), making it a versatile solution.

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