Learning adjacency matrix for dynamic graph neural network
- URL: http://arxiv.org/abs/2310.02606v1
- Date: Wed, 4 Oct 2023 06:42:33 GMT
- Title: Learning adjacency matrix for dynamic graph neural network
- Authors: Osama Ahmad, Omer Abdul Jalil, Usman Nazir, Murtaza Taj
- Abstract summary: We introduce an encoder specifically designed to learn these missing links.
The encoder block processes the Adjacency Matrix (BA) and predicts connections between unconnected nodes.
This matrix is then fed into a Neural Block Adjacency Matrix (STBAM) to capture the complex subtemporal network.
- Score: 3.8561286070836798
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent work, [1] introduced the concept of using a Block Adjacency Matrix
(BA) for the representation of spatio-temporal data. While their method
successfully concatenated adjacency matrices to encapsulate spatio-temporal
relationships in a single graph, it formed a disconnected graph. This
limitation hampered the ability of Graph Convolutional Networks (GCNs) to
perform message passing across nodes belonging to different time steps, as no
temporal links were present. To overcome this challenge, we introduce an
encoder block specifically designed to learn these missing temporal links. The
encoder block processes the BA and predicts connections between previously
unconnected subgraphs, resulting in a Spatio-Temporal Block Adjacency Matrix
(STBAM). This enriched matrix is then fed into a Graph Neural Network (GNN) to
capture the complex spatio-temporal topology of the network. Our evaluations on
benchmark datasets, surgVisDom and C2D2, demonstrate that our method, with
slightly higher complexity, achieves superior results compared to
state-of-the-art results. Our approach's computational overhead remains
significantly lower than conventional non-graph-based methodologies for
spatio-temporal data.
Related papers
- Temporal Aggregation and Propagation Graph Neural Networks for Dynamic
Representation [67.26422477327179]
Temporal graphs exhibit dynamic interactions between nodes over continuous time.
We propose a novel method of temporal graph convolution with the whole neighborhood.
Our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency.
arXiv Detail & Related papers (2023-04-15T08:17:18Z) - Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs [51.51417735550026]
Methods for machine learning on temporal networks generally exhibit at least one of two limitations.
We present a simple method that avoids both shortcomings: construct the line graph of the network, which includes a node for each interaction, and weigh the edges of this graph based on the difference in time between interactions.
Empirical results on real-world networks demonstrate our method's efficacy and efficiency on both edge classification and temporal link prediction.
arXiv Detail & Related papers (2022-09-30T18:24:13Z) - 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) - Scalable Spatiotemporal Graph Neural Networks [14.415967477487692]
Graph neural networks (GNNs) are often the core component of the forecasting architecture.
In most pretemporal GNNs, the computational complexity scales up to a quadratic factor with the length of the sequence times the number of links in the graph.
We propose a scalable architecture that exploits an efficient encoding of both temporal and spatial dynamics.
arXiv Detail & Related papers (2022-09-14T09:47:38Z) - Spatial-Temporal Adaptive Graph Convolution with Attention Network for
Traffic Forecasting [4.1700160312787125]
We propose a novel network, Spatial-Temporal Adaptive graph convolution with Attention Network (STAAN) for traffic forecasting.
Firstly, we adopt an adaptive dependency matrix instead of using a pre-defined matrix during GCN processing to infer the inter-dependencies among nodes.
Secondly, we integrate PW-attention based on graph attention network which is designed for global dependency, and GCN as spatial block.
arXiv Detail & Related papers (2022-06-07T09:08:35Z) - Interpolation-based Correlation Reduction Network for Semi-Supervised
Graph Learning [49.94816548023729]
We propose a novel graph contrastive learning method, termed Interpolation-based Correlation Reduction Network (ICRN)
In our method, we improve the discriminative capability of the latent feature by enlarging the margin of decision boundaries.
By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discnative representation learning.
arXiv Detail & Related papers (2022-06-06T14:26:34Z) - Efficient-Dyn: Dynamic Graph Representation Learning via Event-based
Temporal Sparse Attention Network [2.0047096160313456]
Dynamic graph neural networks have received more and more attention from researchers.
We propose a novel dynamic graph neural network, Efficient-Dyn.
It adaptively encodes temporal information into a sequence of patches with an equal amount of temporal-topological structure.
arXiv Detail & Related papers (2022-01-04T23:52:24Z) - Spatio-Temporal Joint Graph Convolutional Networks for Traffic
Forecasting [75.10017445699532]
Recent have shifted their focus towards formulating traffic forecasting as atemporal graph modeling problem.
We propose a novel approach for accurate traffic forecasting on road networks over multiple future time steps.
arXiv Detail & Related papers (2021-11-25T08:45:14Z) - Binarized Graph Neural Network [65.20589262811677]
We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters.
Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches.
Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
arXiv Detail & Related papers (2020-04-19T09:43:14Z)
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.