Graph Neural Re-Ranking via Corpus Graph

• URL: http://arxiv.org/abs/2406.11720v1
• Date: Mon, 17 Jun 2024 16:38:19 GMT
• Title: Graph Neural Re-Ranking via Corpus Graph
• Authors: Andrea Giuseppe Di Francesco, Christian Giannetti, Nicola Tonellotto, Fabrizio Silvestri,
• Abstract summary: Graph Neural Re-Ranking (GNRR) is a pipeline based on Graph Neural Networks (GNNs) that enables each query to consider documents distribution during inference. We demonstrate that GNNs effectively capture cross-document interactions, improving performance on popular ranking metrics.
• Score: 12.309841763251406
• License:
• Abstract: Re-ranking systems aim to reorder an initial list of documents to satisfy better the information needs associated with a user-provided query. Modern re-rankers predominantly rely on neural network models, which have proven highly effective in representing samples from various modalities. However, these models typically evaluate query-document pairs in isolation, neglecting the underlying document distribution that could enhance the quality of the re-ranked list. To address this limitation, we propose Graph Neural Re-Ranking (GNRR), a pipeline based on Graph Neural Networks (GNNs), that enables each query to consider documents distribution during inference. Our approach models document relationships through corpus subgraphs and encodes their representations using GNNs. Through extensive experiments, we demonstrate that GNNs effectively capture cross-document interactions, improving performance on popular ranking metrics. In TREC-DL19, we observe a relative improvement of 5.8% in Average Precision compared to our baseline. These findings suggest that integrating the GNN segment offers significant advantages, especially in scenarios where understanding the broader context of documents is crucial.

Related papers

• Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks [50.42343781348247]
  We develop a graph Poisson factor analysis (GPFA) which provides analytic conditional posteriors to improve the inference accuracy. We also extend GPFA to a multi-stochastic-layer version named graph Poisson gamma belief network (GPGBN) to capture the hierarchical document relationships at multiple semantic levels. Our models can extract high-quality hierarchical latent document representations and achieve promising performance on various graph analytic tasks.
  arXiv  Detail & Related papers  (2024-10-13T02:22:14Z)
• Graph Neural Network Enhanced Retrieval for Question Answering of LLMs [19.24603296717601]
  Existing retrieval methods divide reference documents into passages, treating them in isolation. These passages, however, are often interrelated, such as passages that are contiguous or share the same keywords. We propose a novel retrieval method, called GNN-Ret, which leverages graph neural networks (GNNs) to enhance retrieval by exploiting the relatedness between passages.
  arXiv  Detail & Related papers  (2024-06-03T17:07:46Z)
• Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis [50.972595036856035]
  We present a code that successfully replicates results from six popular and recent graph recommendation models. We compare these graph models with traditional collaborative filtering models that historically performed well in offline evaluations. By investigating the information flow from users' neighborhoods, we aim to identify which models are influenced by intrinsic features in the dataset structure.
  arXiv  Detail & Related papers  (2023-08-01T09:31:44Z)
• Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking [56.80065604034095]
  We introduce a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant. To evaluate our different integration strategies, we transform four existing information retrieval datasets into the relevance feedback scenario.
  arXiv  Detail & Related papers  (2022-10-19T16:19:37Z)
• Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph [58.99478502486377]
  We propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections.
  arXiv  Detail & Related papers  (2022-09-23T05:24:18Z)
• GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks [68.61934077627085]
  We introduce GNNRank, a modeling framework compatible with any GNN capable of learning digraph embeddings. We show that our methods attain competitive and often superior performance compared with existing approaches.
  arXiv  Detail & Related papers  (2022-02-01T04:19:50Z)
• Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution [3.4806267677524896]
  In mature data repositories, the relationships may be mostly correct but require incremental improvements owing to errors in the original data or in the entity resolution system. This paper proposes a novel method for identifying inconsistent clusters (IC), existing groups of related products that do not belong together. We demonstrate that existing Message Passing neural networks perform well at this task, exceeding traditional graph processing techniques.
  arXiv  Detail & Related papers  (2021-05-12T20:39:22Z)
• Scalable Graph Neural Networks for Heterogeneous Graphs [12.44278942365518]
  Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature smoothing, and have shown competitive results on benchmark tasks. In this work, we ask whether these results can be extended to heterogeneous graphs, which encode multiple types of relationship between different entities.
  arXiv  Detail & Related papers  (2020-11-19T06:03:35Z)
• Policy-GNN: Aggregation Optimization for Graph Neural Networks [60.50932472042379]
  Graph neural networks (GNNs) aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors. It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features. We propose Policy-GNN, a meta-policy framework that models the sampling procedure and message passing of GNNs into a combined learning process.
  arXiv  Detail & Related papers  (2020-06-26T17:03:06Z)
• Adaptive Universal Generalized PageRank Graph Neural Network [36.850433364139924]
  Graph neural networks (GNNs) are designed to exploit both sources of evidence but they do not optimally trade-off their utility. We introduce a new Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR weights. GPR-GNN offers significant performance improvement compared to existing techniques on both synthetic and benchmark data.
  arXiv  Detail & Related papers  (2020-06-14T19:27:39Z)

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.