Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)

• URL: http://arxiv.org/abs/2108.03400v1
• Date: Sat, 7 Aug 2021 08:32:40 GMT
• Title: Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)
• Authors: Bahareh Najafi, Saeedeh Parsaeefard, Alberto Leon-Garcia
• Abstract summary: Temporal Multilayered Position-aware Graph Neural Network (TMP-GNN) is a node embedding approach for dynamic graph. We evaluate the performance of TMP-GNN on two different representations of temporal multilayered graphs. We incorporate TMP-GNN into a deep learning framework to estimate missing data and compare the performance with their corresponding competent GNNs.
• Score: 5.936402320555635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: GNNs have been proven to perform highly effective in various node-level, edge-level, and graph-level prediction tasks in several domains. Existing approaches mainly focus on static graphs. However, many graphs change over time with their edge may disappear, or node or edge attribute may alter from one time to the other. It is essential to consider such evolution in representation learning of nodes in time varying graphs. In this paper, we propose a Temporal Multilayered Position-aware Graph Neural Network (TMP-GNN), a node embedding approach for dynamic graph that incorporates the interdependence of temporal relations into embedding computation. We evaluate the performance of TMP-GNN on two different representations of temporal multilayered graphs. The performance is assessed against the most popular GNNs on node-level prediction tasks. Then, we incorporate TMP-GNN into a deep learning framework to estimate missing data and compare the performance with their corresponding competent GNNs from our former experiment, and a baseline method. Experimental results on four real-world datasets yield up to 58% of lower ROC AUC for pairwise node classification task, and 96% of lower MAE in missing feature estimation, particularly for graphs with a relatively high number of nodes and lower mean degree of connectivity.

Related papers

• Deep Graph Neural Networks via Flexible Subgraph Aggregation [50.034313206471694]
  Graph neural networks (GNNs) can learn from graph-structured data and learn the representation of nodes through aggregating neighborhood information. In this paper, we evaluate the expressive power of GNNs from the perspective of subgraph aggregation. We propose a sampling-based node-level residual module (SNR) that can achieve a more flexible utilization of different hops of subgraph aggregation.
  arXiv  Detail & Related papers  (2023-05-09T12:03:42Z)
• Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions [51.597480162777074]
  Graph neural networks (GNNs) rely on the message passing paradigm to propagate node features and build interactions. Recent works point out that different graph learning tasks require different ranges of interactions between nodes. We study two common graph construction methods in scientific domains, i.e., emphK-nearest neighbor (KNN) graphs and emphfully-connected (FC) graphs.
  arXiv  Detail & Related papers  (2022-05-15T11:38:14Z)
• Neural Graph Matching for Pre-training Graph Neural Networks [72.32801428070749]
  Graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. We present a novel Graph Matching based GNN Pre-Training framework, called GMPT. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training.
  arXiv  Detail & Related papers  (2022-03-03T09:53:53Z)
• Graph Neural Networks with Feature and Structure Aware Random Walk [5.431036185361236]
  We show that in typical heterphilous graphs, the edges may be directed, and whether to treat the edges as is or simply make them undirected greatly affects the performance of the GNN models. We develop a model that adaptively learns the directionality of the graph, and exploits the underlying long-distance correlations between nodes.
  arXiv  Detail & Related papers  (2021-11-19T08:54:21Z)
• Hierarchical graph neural nets can capture long-range interactions [8.067880298298185]
  We study hierarchical message passing models that leverage a multi-resolution representation of a given graph. This facilitates learning of features that span large receptive fields without loss of local information. We introduce Hierarchical Graph Net (HGNet), which for any two connected nodes guarantees existence of message-passing paths of at most logarithmic length.
  arXiv  Detail & Related papers  (2021-07-15T16:24:22Z)
• Multi-grained Semantics-aware Graph Neural Networks [13.720544777078642]
  Graph Neural Networks (GNNs) are powerful techniques in representation learning for graphs. This work proposes a unified model, AdamGNN, to interactively learn node and graph representations. Experiments on 14 real-world graph datasets show that AdamGNN can significantly outperform 17 competing models on both node- and graph-wise tasks.
  arXiv  Detail & Related papers  (2020-10-01T07:52:06Z)
• Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning [63.97983530843762]
  Graph Neural Networks (GNNs) have achieved great success in graph representation learning. GNNs generate identical representations for graph substructures that may in fact be very different. More powerful GNNs, proposed recently by mimicking higher-order tests, are inefficient as they cannot sparsity of underlying graph structure. We propose Distance Depiction (DE) as a new class of graph representation learning.
  arXiv  Detail & Related papers  (2020-08-31T23:15:40Z)
• Isometric Graph Neural Networks [5.306334746787569]
  We propose a technique to learn Isometric Graph Neural Networks (IGNN) IGNN requires changing the input representation space and loss function to enable any GNN algorithm to generate representations that reflect distances between nodes. We observe a consistent and substantial improvement as high as 400% in Kendall's Tau (KT)
  arXiv  Detail & Related papers  (2020-06-16T22:51:13Z)
• Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs [54.13919050090926]
  We propose an end-to-end structural temporal Graph Neural Network model for detecting anomalous edges in dynamic graphs. In particular, we first extract the $h$-hop enclosing subgraph centered on the target edge and propose the node labeling function to identify the role of each node in the subgraph. Based on the extracted features, we utilize Gated recurrent units (GRUs) to capture the temporal information for anomaly detection.
  arXiv  Detail & Related papers  (2020-05-15T09:17:08Z)
• 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.