Graph Neural Networks for Multi-Robot Active Information Acquisition

• URL: http://arxiv.org/abs/2209.12091v1
• Date: Sat, 24 Sep 2022 21:45:06 GMT
• Title: Graph Neural Networks for Multi-Robot Active Information Acquisition
• Authors: Mariliza Tzes, Nikolaos Bousias, Evangelos Chatzipantazis, George J. Pappas
• Abstract summary: A team of mobile robots, communicating through an underlying graph, estimates a hidden state expressing a phenomenon of interest. Existing approaches are either not scalable, unable to handle dynamic phenomena or not robust to changes in the communication graph. We propose an Information-aware Graph Block Network (I-GBNet) that aggregates information over the graph representation and provides sequential-decision making in a distributed manner.
• Score: 15.900385823366117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the Multi-Robot Active Information Acquisition (AIA) problem, where a team of mobile robots, communicating through an underlying graph, estimates a hidden state expressing a phenomenon of interest. Applications like target tracking, coverage and SLAM can be expressed in this framework. Existing approaches, though, are either not scalable, unable to handle dynamic phenomena or not robust to changes in the communication graph. To counter these shortcomings, we propose an Information-aware Graph Block Network (I-GBNet), an AIA adaptation of Graph Neural Networks, that aggregates information over the graph representation and provides sequential-decision making in a distributed manner. The I-GBNet, trained via imitation learning with a centralized sampling-based expert solver, exhibits permutation equivariance and time invariance, while harnessing the superior scalability, robustness and generalizability to previously unseen environments and robot configurations. Experiments on significantly larger graphs and dimensionality of the hidden state and more complex environments than those seen in training validate the properties of the proposed architecture and its efficacy in the application of localization and tracking of dynamic targets.

Related papers

• Task-Oriented Communication for Graph Data: A Graph Information Bottleneck Approach [12.451324619122405]
  This paper introduces a method to extract a smaller, task-focused subgraph that maintains key information while reducing communication overhead. Our approach utilizes graph neural networks (GNNs) and the graph information bottleneck (GIB) principle to create a compact, informative, and robust graph representation suitable for transmission.
  arXiv  Detail & Related papers  (2024-09-04T14:01:56Z)
• Malicious Internet Entity Detection Using Local Graph Inference [0.4893345190925178]
  Detection of malicious behavior in a large network is a challenging problem for machine learning in computer security. Current cybersec-tailored approaches are still limited in expressivity, and methods successful in other domains do not scale well for large volumes of data. This work proposes a new perspective for learning from graph data that is modeling network entity interactions as a large heterogeneous graph.
  arXiv  Detail & Related papers  (2024-08-06T16:35:25Z)
• Gradient Transformation: Towards Efficient and Model-Agnostic Unlearning for Dynamic Graph Neural Networks [66.70786325911124]
  Graph unlearning has emerged as an essential tool for safeguarding user privacy and mitigating the negative impacts of undesirable data. With the increasing prevalence of DGNNs, it becomes imperative to investigate the implementation of dynamic graph unlearning. We propose an effective, efficient, model-agnostic, and post-processing method to implement DGNN unlearning.
  arXiv  Detail & Related papers  (2024-05-23T10:26:18Z)
• DynamicGlue: Epipolar and Time-Informed Data Association in Dynamic Environments using Graph Neural Networks [13.42760841894735]
  We propose a graph neural network-based sparse feature matching network to perform robust matching under challenging conditions. We employ a similar scheme of attentional aggregation over graph edges to enhance keypoint representations as state-of-the-art feature-matching networks. A series of experiments show the superior performance of our network as it excludes keypoints on moving objects compared to state-of-the-art feature matching networks.
  arXiv  Detail & Related papers  (2024-03-17T23:23:40Z)
• Explainable Spatio-Temporal Graph Neural Networks [16.313146933922752]
  We propose an Explainable Spatio-Temporal Graph Neural Networks (STGNN) framework that enhances STGNNs with inherent explainability. Our framework integrates a unified-temporal graph attention network with a positional information fusion layer as the STG encoder and decoder. We demonstrate that STExplainer outperforms state-of-the-art baselines in terms of predictive accuracy and explainability metrics.
  arXiv  Detail & Related papers  (2023-10-26T04:47:28Z)
• Network Alignment with Transferable Graph Autoencoders [79.89704126746204]
  We propose a novel graph autoencoder architecture designed to extract powerful and robust node embeddings. We prove that the generated embeddings are associated with the eigenvalues and eigenvectors of the graphs. Our proposed framework also leverages transfer learning and data augmentation to achieve efficient network alignment at a very large scale without retraining.
  arXiv  Detail & Related papers  (2023-10-05T02:58:29Z)
• Dynamic Graph Message Passing Networks for Visual Recognition [112.49513303433606]
  Modelling long-range dependencies is critical for scene understanding tasks in computer vision. A fully-connected graph is beneficial for such modelling, but its computational overhead is prohibitive. We propose a dynamic graph message passing network, that significantly reduces the computational complexity.
  arXiv  Detail & Related papers  (2022-09-20T14:41:37Z)
• Affinity-Aware Graph Networks [9.888383815189176]
  Graph Neural Networks (GNNs) have emerged as a powerful technique for learning on relational data. We explore the use of affinity measures as features in graph neural networks. We propose message passing networks based on these features and evaluate their performance on a variety of node and graph property prediction tasks.
  arXiv  Detail & Related papers  (2022-06-23T18:51:35Z)
• GraphMI: Extracting Private Graph Data from Graph Neural Networks [59.05178231559796]
  We present textbfGraph textbfModel textbfInversion attack (GraphMI), which aims to extract private graph data of the training graph by inverting GNN. Specifically, we propose a projected gradient module to tackle the discreteness of graph edges while preserving the sparsity and smoothness of graph features. We design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
  arXiv  Detail & Related papers  (2021-06-05T07:07:52Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

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.