Path-Based Gradient Boosting for Graph-Level Prediction

• URL: http://arxiv.org/abs/2605.08102v1
• Date: Sun, 26 Apr 2026 07:59:33 GMT
• Title: Path-Based Gradient Boosting for Graph-Level Prediction
• Abstract summary: PathBoost learns discriminative path-based features directly from the input graph structure.<n>We compared PathBoost to graph neural networks and graph kernel approaches on several benchmark datasets.<n>Overall, the results demonstrate that path-based boosting methods can be competitive with more complex black-box approaches.
• Score: 0.6181093777643575
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We propose PathBoost, a gradient tree boosting method for graph-level classification and regression that learns discriminative path-based features directly from the input graph structure. Building on a previous work, which was tailored to a specific chemistry application, PathBoost introduces three key extensions: (i) adaptation to binary classification through gradient boosting with a logistic loss, (ii) incorporation of multiple node and edge attributes into the path feature space via a prefix-based decomposition, and (iii) automatic anchor node selection based on categorical attribute diversity, eliminating the need for the user to specify the starting point of the considered path features. We compared PathBoost to graph neural networks and graph kernel approaches on several benchmark datasets, obtaining better results in half of them, and comparable results in the rest. PathBoost shows better performances on graphs with larger average node counts. Overall, the results demonstrate that path-based boosting methods can be competitive with more complex black-box approaches.

Related papers

• path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting [0.6181093777643575]
  Path_boost is a Python package for interpretable supervised learning on graph-structured input data.<n>PathBoost produces an additive prediction model over path-based features.<n>It supports both regression and binary classification.
  arXiv  Detail & Related papers  (2026-07-08T21:31:48Z)
• Gradient boosting with vector-valued leafs [0.0]
  Gradient boosting in the form of decision tree ensembles has successfully been applied to a variety of problems.<n>This work extends the usual gradient boosting framework to functions of vector inputs and sketches a simple algorithm that can be used efficiently with histogram-based decision trees.
  arXiv  Detail & Related papers  (2026-06-28T10:42:29Z)
• STAR: Semantic-Tuned and Tail-Adaptive Retriever for Graph-Augmented Generation [69.66045538889777]
  We propose STAR, a semantic-tuned and tail-adaptive retriever for GraphRAG.<n> STAR consistently outperforms baselines, achieving average retrieval performance gains of 1.8%.
  arXiv  Detail & Related papers  (2026-04-11T10:16:51Z)
• FUSE: Fast Semi-Supervised Node Embedding Learning via Structural and Label-Aware Optimization [0.0]
  We introduce a fast semi-supervised embedding framework that jointly optimize three complementary objectives.<n>On standard benchmarks, our method consistently achieves classification accuracy at par with or superior to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-10-13T10:39:58Z)
• How to guess a gradient [68.98681202222664]
  We show that gradients are more structured than previously thought. Exploiting this structure can significantly improve gradient-free optimization schemes. We highlight new challenges in overcoming the large gap between optimizing with exact gradients and guessing the gradients.
  arXiv  Detail & Related papers  (2023-12-07T21:40:44Z)
• EGRC-Net: Embedding-induced Graph Refinement Clustering Network [66.44293190793294]
  We propose a novel graph clustering network called Embedding-Induced Graph Refinement Clustering Network (EGRC-Net) EGRC-Net effectively utilizes the learned embedding to adaptively refine the initial graph and enhance the clustering performance. Our proposed methods consistently outperform several state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-19T09:08:43Z)
• Hub-aware Random Walk Graph Embedding Methods for Classification [44.99833362998488]
  We propose two novel graph embedding algorithms based on random walks that are specifically designed for the node classification problem. The proposed methods are experimentally evaluated by analyzing the classification performance of three classification algorithms trained on embeddings of real-world networks.
  arXiv  Detail & Related papers  (2022-09-15T20:41:18Z)
• Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training [82.68805025636165]
  We propose to select positive graph instances directly from existing graphs in the training set. Our selection is based on certain domain-specific pair-wise similarity measurements. Besides, we develop an adaptive node-level pre-training method to dynamically mask nodes to distribute them evenly in the graph.
  arXiv  Detail & Related papers  (2022-06-23T20:12:51Z)
• 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 Contrastive Learning with Adaptive Augmentation [23.37786673825192]
  We propose a novel graph contrastive representation learning method with adaptive augmentation. Specifically, we design augmentation schemes based on node centrality measures to highlight important connective structures. Our proposed method consistently outperforms existing state-of-the-art baselines and even surpasses some supervised counterparts.
  arXiv  Detail & Related papers  (2020-10-27T15:12:21Z)
• Anisotropic Graph Convolutional Network for Semi-supervised Learning [7.843067454030999]
  Graph convolutional networks learn effective node embeddings that have proven to be useful in achieving high-accuracy prediction results. These networks suffer from the issue of over-smoothing and shrinking effect of the graph due in large part to the fact that they diffuse features across the edges of the graph using a linear Laplacian flow. We propose an anisotropic graph convolutional network for semi-supervised node classification by introducing a nonlinear function that captures informative features from nodes, while preventing oversmoothing.
  arXiv  Detail & Related papers  (2020-10-20T13:56:03Z)
• Smoothness Sensor: Adaptive Smoothness-Transition Graph Convolutions for Attributed Graph Clustering [10.905770964670191]
  We propose a smoothness sensor for attributed graph clustering based on adaptive smoothness-transition graph convolutions. As an alternative to graph-level smoothness, a novel fine-gained node-wise level assessment of smoothness is proposed. Experiments show that the proposed methods significantly outperform 12 other state-of-the-art baselines in terms of three different metrics.
  arXiv  Detail & Related papers  (2020-09-12T08:12:27Z)
• Channel-Directed Gradients for Optimization of Convolutional Neural Networks [50.34913837546743]
  We introduce optimization methods for convolutional neural networks that can be used to improve existing gradient-based optimization in terms of generalization error. We show that defining the gradients along the output channel direction leads to a performance boost, while other directions can be detrimental.
  arXiv  Detail & Related papers  (2020-08-25T00:44:09Z)
• Graph Inference Learning for Semi-supervised Classification [50.55765399527556]
  We propose a Graph Inference Learning framework to boost the performance of semi-supervised node classification. For learning the inference process, we introduce meta-optimization on structure relations from training nodes to validation nodes. Comprehensive evaluations on four benchmark datasets demonstrate the superiority of our proposed GIL when compared against state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-17T02:52:30Z)

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.