Learning Hierarchical Relational Representations through Relational Convolutions

• URL: http://arxiv.org/abs/2310.03240v3
• Date: Thu, 26 Sep 2024 22:45:29 GMT
• Title: Learning Hierarchical Relational Representations through Relational Convolutions
• Authors: Awni Altabaa, John Lafferty,
• Abstract summary: We introduce "relational convolutional networks", a neural architecture equipped with computational mechanisms that capture progressively more complex relational features. A key component of this framework is a novel operation that captures relational patterns in groups of objects by convolving graphlet filters. We present the motivation and details of the architecture, together with a set of experiments to demonstrate how relational convolutional networks can provide an effective framework for modeling relational tasks that have hierarchical structure.
• Score: 2.5322020135765464
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An evolving area of research in deep learning is the study of architectures and inductive biases that support the learning of relational feature representations. In this paper, we address the challenge of learning representations of hierarchical relations--that is, higher-order relational patterns among groups of objects. We introduce "relational convolutional networks", a neural architecture equipped with computational mechanisms that capture progressively more complex relational features through the composition of simple modules. A key component of this framework is a novel operation that captures relational patterns in groups of objects by convolving graphlet filters--learnable templates of relational patterns--against subsets of the input. Composing relational convolutions gives rise to a deep architecture that learns representations of higher-order, hierarchical relations. We present the motivation and details of the architecture, together with a set of experiments to demonstrate how relational convolutional networks can provide an effective framework for modeling relational tasks that have hierarchical structure.

Related papers

• Geometric Relational Embeddings [19.383110247906256]
  We propose relational embeddings, a paradigm of embeddings that respect the underlying symbolic structures. Results obtained from benchmark real-world datasets demonstrate the efficacy of geometric relational embeddings.
  arXiv  Detail & Related papers  (2024-09-18T22:02:24Z)
• HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling [39.14392943549792]
  We propose a novel approach called Hierarchical Prompt Tuning (HPT), enabling simultaneous modeling of both structured and conventional linguistic knowledge. We introduce a relationship-guided attention module to capture pair-wise associations among entities and attributes for low-level prompt learning. By incorporating high-level and global-level prompts modeling overall semantics, the proposed hierarchical structure forges cross-level interlinks and empowers the model to handle more complex and long-term relationships.
  arXiv  Detail & Related papers  (2024-08-27T06:50:28Z)
• Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction [121.65152276851619]
  We show that semantic correlations between relations are inherently edge-level and entity-independent. We propose a novel subgraph-based method, namely TACO, to model Topology-Aware COrrelations between relations. To further exploit the potential of RCN, we propose Complete Common Neighbor induced subgraph.
  arXiv  Detail & Related papers  (2023-09-20T08:11:58Z)
• ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy [65.5580334698777]
  ViRel is a method for unsupervised discovery and learning of Visual Relations with graph-level analogy. We show that our method achieves above 95% accuracy in relation classification. We further generalizes to unseen tasks with more complicated relational structures.
  arXiv  Detail & Related papers  (2022-07-04T16:56:45Z)
• On Neural Architecture Inductive Biases for Relational Tasks [76.18938462270503]
  We introduce a simple architecture based on similarity-distribution scores which we name Compositional Network generalization (CoRelNet) We find that simple architectural choices can outperform existing models in out-of-distribution generalizations.
  arXiv  Detail & Related papers  (2022-06-09T16:24:01Z)
• Topological Deep Learning: Going Beyond Graph Data [26.325857542512047]
  We present a unifying deep learning framework built upon a richer data structure that includes widely adopted topological domains. Specifically, we first introduce complexes, a novel type of topological domain. We develop a class of message-passing complex neural networks (CCNNs) focusing primarily on attention-based CCNNs.
  arXiv  Detail & Related papers  (2022-06-01T16:21:28Z)
• DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy [4.607120217372668]
  We develop a novel knowledge graph embedding method, named DensE, to provide an improved modeling scheme for the complex composition patterns of relations. Our method decomposes each relation into an SO(3) group-based rotation operator and a scaling operator in the three dimensional (3-D) Euclidean space. Experimental results on multiple benchmark knowledge graphs show that DensE outperforms the current state-of-the-art models for missing link prediction.
  arXiv  Detail & Related papers  (2020-08-11T06:45:50Z)
• Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification [50.83222170524406]
  We study the intrinsic difficulty in graph classification under the unified concept of resolution dilemmas'' We propose SLIM'', an inductive neural network model for Structural Landmarking and Interaction Modelling.
  arXiv  Detail & Related papers  (2020-06-29T01:01:42Z)
• Adaptive Interaction Modeling via Graph Operations Search [109.45125932109454]
  We automate the process of structures design to learn adaptive structures for interaction modeling. We experimentally demonstrate that our architecture search framework learns to construct adaptive interaction modeling structures. Our method achieves competitive performance with state-of-the-arts.
  arXiv  Detail & Related papers  (2020-05-05T13:01:09Z)
• Hierarchical Conditional Relation Networks for Video Question Answering [62.1146543269993]
  We introduce a general-purpose reusable neural unit called Conditional Relation Network (CRN) CRN serves as a building block to construct more sophisticated structures for representation and reasoning over video. Our evaluations on well-known datasets achieved new SoTA results, demonstrating the impact of building a general-purpose reasoning unit on complex domains such as VideoQA.
  arXiv  Detail & Related papers  (2020-02-25T07:00:48Z)

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.