Learning to Identify Graphs from Node Trajectories in Multi-Robot
Networks
- URL: http://arxiv.org/abs/2307.04374v2
- Date: Sat, 21 Oct 2023 11:28:41 GMT
- Title: Learning to Identify Graphs from Node Trajectories in Multi-Robot
Networks
- Authors: Eduardo Sebastian, Thai Duong, Nikolay Atanasov, Eduardo Montijano,
Carlos Sagues
- Abstract summary: We propose a learning-based approach that efficiently uncovers graph topologies with global convergence guarantees.
We demonstrate the effectiveness of our approach in identifying graphs in multi-robot formation and flocking tasks.
- Score: 15.36505600407192
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The graph identification problem consists of discovering the interactions
among nodes in a network given their state/feature trajectories. This problem
is challenging because the behavior of a node is coupled to all the other nodes
by the unknown interaction model. Besides, high-dimensional and nonlinear state
trajectories make it difficult to identify if two nodes are connected. Current
solutions rely on prior knowledge of the graph topology and the dynamic
behavior of the nodes, and hence, have poor generalization to other network
configurations. To address these issues, we propose a novel learning-based
approach that combines (i) a strongly convex program that efficiently uncovers
graph topologies with global convergence guarantees and (ii) a self-attention
encoder that learns to embed the original state trajectories into a feature
space and predicts appropriate regularizers for the optimization program. In
contrast to other works, our approach can identify the graph topology of unseen
networks with new configurations in terms of number of nodes, connectivity or
state trajectories. We demonstrate the effectiveness of our approach in
identifying graphs in multi-robot formation and flocking tasks.
Related papers
- Improving Graph Neural Networks by Learning Continuous Edge Directions [0.0]
Graph Neural Networks (GNNs) traditionally employ a message-passing mechanism that resembles diffusion over undirected graphs.
Our key insight is to assign fuzzy edge directions to the edges of a graph so that features can preferentially flow in one direction between nodes.
We propose a general framework, called Continuous Edge Direction (CoED) GNN, for learning on graphs with fuzzy edges.
arXiv Detail & Related papers (2024-10-18T01:34:35Z) - Online Learning Of Expanding Graphs [14.952056744888916]
This paper addresses the problem of online network inference for expanding graphs from a stream of signals.
We introduce a strategy that enables different types of updates for nodes that just joined the network and for previously existing nodes.
arXiv Detail & Related papers (2024-09-13T09:20:42Z) - Graph Transformer GANs with Graph Masked Modeling for Architectural
Layout Generation [153.92387500677023]
We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations.
The proposed graph Transformer encoder combines graph convolutions and self-attentions in a Transformer to model both local and global interactions.
We also propose a novel self-guided pre-training method for graph representation learning.
arXiv Detail & Related papers (2024-01-15T14:36:38Z) - GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy [21.553180564868306]
GraphRARE is a framework built upon node relative entropy and deep reinforcement learning.
An innovative node relative entropy is used to measure mutual information between node pairs.
A deep reinforcement learning-based algorithm is developed to optimize the graph topology.
arXiv Detail & Related papers (2023-12-15T11:30:18Z) - 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) - BOURNE: Bootstrapped Self-supervised Learning Framework for Unified
Graph Anomaly Detection [50.26074811655596]
We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE)
By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies.
BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
arXiv Detail & Related papers (2023-07-28T00:44:57Z) - NodeFormer: A Scalable Graph Structure Learning Transformer for Node
Classification [70.51126383984555]
We introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes.
The efficient computation is enabled by a kernerlized Gumbel-Softmax operator.
Experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs.
arXiv Detail & Related papers (2023-06-14T09:21:15Z) - Graph Transformer GANs for Graph-Constrained House Generation [223.739067413952]
We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations.
The GTGAN learns effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task.
arXiv Detail & Related papers (2023-03-14T20:35:45Z) - Reasoning Graph Networks for Kinship Verification: from Star-shaped to
Hierarchical [85.0376670244522]
We investigate the problem of facial kinship verification by learning hierarchical reasoning graph networks.
We develop a Star-shaped Reasoning Graph Network (S-RGN) to exploit more powerful and flexible capacity.
We also develop a Hierarchical Reasoning Graph Network (H-RGN) to exploit more powerful and flexible capacity.
arXiv Detail & Related papers (2021-09-06T03:16:56Z)
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.