Some Insights of Construction of Feature Graph to Learn Pairwise Feature Interactions with Graph Neural Networks

• URL: http://arxiv.org/abs/2502.13471v1
• Date: Wed, 19 Feb 2025 06:47:23 GMT
• Title: Some Insights of Construction of Feature Graph to Learn Pairwise Feature Interactions with Graph Neural Networks
• Authors: Phaphontee Yamchote, Saw Nay Htet Win, Chainarong Amornbunchornvej, Thanapon Noraset,
• Abstract summary: We focus on pairwise interactions and investigate their importance in constructing feature graphs for Graph Neural Networks (GNNs) We uncover that edges between interacting features are important for enabling GNNs to model feature interactions effectively. We also observe that including non-interaction edges can act as noise, degrading model performance.
• Score: 0.24999074238880484
• License:
• Abstract: Feature interaction is crucial in predictive machine learning models, as it captures the relationships between features that influence model performance. In this work, we focus on pairwise interactions and investigate their importance in constructing feature graphs for Graph Neural Networks (GNNs). Rather than proposing new methods, we leverage existing GNN models and tools to explore the relationship between feature graph structures and their effectiveness in modeling interactions. Through experiments on synthesized datasets, we uncover that edges between interacting features are important for enabling GNNs to model feature interactions effectively. We also observe that including non-interaction edges can act as noise, degrading model performance. Furthermore, we provide theoretical support for sparse feature graph selection using the Minimum Description Length (MDL) principle. We prove that feature graphs retaining only necessary interaction edges yield a more efficient and interpretable representation than complete graphs, aligning with Occam's Razor. Our findings offer both theoretical insights and practical guidelines for designing feature graphs that improve the performance and interpretability of GNN models.

Related papers

• Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements [54.006506479865344]
  We propose a unified evaluation framework for graph-level Graph Neural Networks (GNNs) This framework provides a standardized setting to evaluate GNNs across diverse datasets. We also propose a novel GNN model with enhanced expressivity and generalization capabilities.
  arXiv  Detail & Related papers  (2025-01-01T08:48:53Z)
• TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation Learning [7.879217146851148]
  We propose an innovative Graph Neural Network (GNN) architecture that integrates a Top-m attention mechanism aggregation component and a neighborhood aggregation component. To assess the effectiveness of our proposed model, we have applied it to citation sentiment prediction, a novel task previously unexplored in the GNN field.
  arXiv  Detail & Related papers  (2024-11-23T05:31:25Z)
• Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning [2.492884361833709]
  We present an exhaustive review of the latest advancements in graph representation learning and Graph Neural Networks (GNNs) GNNs, tailored to handle graph-structured data, excel in deriving insights and predictions from intricate relational information. Our work delves into the capabilities of GNNs, examining their foundational designs and their application in addressing real-world challenges.
  arXiv  Detail & Related papers  (2024-11-09T19:10:33Z)
• DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations [62.04558318166396]
  Graph neural networks (GNNs) demonstrate a robust capability for representation learning on graphs with complex structures. A novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced to obtain a more comprehensive embedding representation of nodes. Experimental results conducted on several graph benchmark datasets verify DGNN's superiority in node classification task.
  arXiv  Detail & Related papers  (2024-01-28T06:43:13Z)
• Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet Energy [103.74640329539389]
  We propose a deep FS method that simultaneously conducts feature selection and differentiable $ k $-NN graph learning. We employ Optimal Transport theory to address the non-differentiability issue of learning $ k $-NN graphs in neural networks. We validate the effectiveness of our model with extensive experiments on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-05-21T08:15:55Z)
• On the Ability of Graph Neural Networks to Model Interactions Between Vertices [14.909298522361306]
  Graph neural networks (GNNs) are widely used for modeling complex interactions between entities represented as vertices of a graph. Despite recent efforts to theoretically analyze the expressive power of GNNs, a formal characterization of their ability to model interactions is lacking.
  arXiv  Detail & Related papers  (2022-11-29T18:58:07Z)
• Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions [51.597480162777074]
  Graph neural networks (GNNs) rely on the message passing paradigm to propagate node features and build interactions. Recent works point out that different graph learning tasks require different ranges of interactions between nodes. We study two common graph construction methods in scientific domains, i.e., emphK-nearest neighbor (KNN) graphs and emphfully-connected (FC) graphs.
  arXiv  Detail & Related papers  (2022-05-15T11:38:14Z)
• GraphFM: Graph Factorization Machines for Feature Interaction Modeling [27.307086868266012]
  We propose a novel approach, Graph Factorization Machine (GraphFM), by naturally representing features in the graph structure. In particular, we design a mechanism to select the beneficial feature interactions and formulate them as edges between features. The proposed model integrates the interaction function of FM into the feature aggregation strategy of Graph Neural Network (GNN)
  arXiv  Detail & Related papers  (2021-05-25T12:10:54Z)
• Graph Neural Networks Including Sparse Interpretability [0.0]
  We present a model-agnostic framework for interpreting important graph structure and node features. Our GISST models achieve superior node feature and edge explanation precision in synthetic datasets.
  arXiv  Detail & Related papers  (2020-06-30T21:35:55Z)
• GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions [70.9481395807354]
  We propose a Graph of Graphs Neural Network (GoGNN), which extracts the features in both structured entity graphs and the entity interaction graph in a hierarchical way. GoGNN outperforms the state-of-the-art methods on two representative structured entity interaction prediction tasks.
  arXiv  Detail & Related papers  (2020-05-12T03:46:15Z)
• Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions [153.6357310444093]
  Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data. We argue that existing designs of GCN forgo modeling cross features, making GCN less effective for tasks or data where cross features are important. We design a new operator named Cross-feature Graph Convolution, which explicitly models the arbitrary-order cross features with complexity linear to feature dimension and order size.
  arXiv  Detail & Related papers  (2020-03-05T13:05:27Z)

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.