Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation

• URL: http://arxiv.org/abs/2305.18374v1
• Date: Sun, 28 May 2023 05:34:50 GMT
• Title: Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation
• Authors: Edoardo D'Amico, Aonghus Lawlor, Neil Hurley
• Abstract summary: We investigate the effect of using graph convolution throughout the user and item representation learning processes. We present an approach that directly leverages the eigenvectors to emulate the solution obtained through graph convolution.
• Score: 1.8047694351309205
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The use of graph convolution in the development of recommender system algorithms has recently achieved state-of-the-art results in the collaborative filtering task (CF). While it has been demonstrated that the graph convolution operation is connected to a filtering operation on the graph spectral domain, the theoretical rationale for why this leads to higher performance on the collaborative filtering problem remains unknown. The presented work makes two contributions. First, we investigate the effect of using graph convolution throughout the user and item representation learning processes, demonstrating how the latent features learned are pushed from the filtering operation into the subspace spanned by the eigenvectors associated with the highest eigenvalues of the normalised adjacency matrix, and how vectors lying on this subspace are the optimal solutions for an objective function related to the sum of the prediction function over the training data. Then, we present an approach that directly leverages the eigenvectors to emulate the solution obtained through graph convolution, eliminating the requirement for a time-consuming gradient descent training procedure while also delivering higher performance on three real-world datasets.

Related papers

• Amplify Graph Learning for Recommendation via Sparsity Completion [16.32861024767423]
  Graph learning models have been widely deployed in collaborative filtering (CF) based recommendation systems. Due to the issue of data sparsity, the graph structure of the original input lacks potential positive preference edges. We propose an Amplify Graph Learning framework based on Sparsity Completion (called AGL-SC)
  arXiv  Detail & Related papers  (2024-06-27T08:26:20Z)
• Spectral Augmentations for Graph Contrastive Learning [50.149996923976836]
  Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learning for pre-training. We propose a set of well-motivated graph transformation operations to provide a bank of candidates when constructing augmentations for a graph contrastive objective.
  arXiv  Detail & Related papers  (2023-02-06T16:26:29Z)
• Learning Optimal Graph Filters for Clustering of Attributed Graphs [20.810096547938166]
  Many real-world systems can be represented as graphs where the different entities in the system are presented by nodes and their interactions by edges. An important task in studying large datasets with graphical structure is graph clustering. We introduce a graph signal processing based approach, where we learn the parameters of Finite Impulse Response (FIR) and Autoregressive Moving Average (ARMA) graph filters optimized for clustering.
  arXiv  Detail & Related papers  (2022-11-09T01:49:23Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• Graph Kernel Neural Networks [53.91024360329517]
  We propose to use graph kernels, i.e. kernel functions that compute an inner product on graphs, to extend the standard convolution operator to the graph domain. This allows us to define an entirely structural model that does not require computing the embedding of the input graph. Our architecture allows to plug-in any type of graph kernels and has the added benefit of providing some interpretability.
  arXiv  Detail & Related papers  (2021-12-14T14:48:08Z)
• Adaptive Filters in Graph Convolutional Neural Networks [0.0]
  Graph Neural Networks (GNN) have gained a high interest because of their potential in processing graph-structured data. This paper presents a novel method to adapt the behaviour of a ConvGNN to the input proposing a method to perform spatial convolution on graphs.
  arXiv  Detail & Related papers  (2021-05-21T14:36:39Z)
• Multilayer Clustered Graph Learning [66.94201299553336]
  We use contrastive loss as a data fidelity term, in order to properly aggregate the observed layers into a representative graph. Experiments show that our method leads to a clustered clusters w.r.t. We learn a clustering algorithm for solving clustering problems.
  arXiv  Detail & Related papers  (2020-10-29T09:58:02Z)
• Pseudoinverse Graph Convolutional Networks: Fast Filters Tailored for Large Eigengaps of Dense Graphs and Hypergraphs [0.0]
  Graph Convolutional Networks (GCNs) have proven to be successful tools for semi-supervised classification on graph-based datasets. We propose a new GCN variant whose three-part filter space is targeted at dense graphs.
  arXiv  Detail & Related papers  (2020-08-03T08:48:41Z)
• Adaptive Graph Encoder for Attributed Graph Embedding [36.06427854846497]
  Attributed graph embedding learns vector representations from graph topology and node features. We propose Adaptive Graph (AGE), a novel attributed graph embedding framework. We conduct experiments using four public benchmark datasets to validate AGE on node clustering and link prediction tasks.
  arXiv  Detail & Related papers  (2020-07-03T10:20:34Z)
• Graph Pooling with Node Proximity for Hierarchical Representation Learning [80.62181998314547]
  We propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology. Results show that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
  arXiv  Detail & Related papers  (2020-06-19T13:09:44Z)
• Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach [55.44107800525776]
  Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models. In this paper, we revisit GCN based Collaborative Filtering (CF) based Recommender Systems (RS) We show that removing non-linearities would enhance recommendation performance, consistent with the theories in simple graph convolutional networks. We propose a residual network structure that is specifically designed for CF with user-item interaction modeling.
  arXiv  Detail & Related papers  (2020-01-28T04:41:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.