2 research outputs found
Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation
Graph similarity computation aims to predict a similarity score between one
pair of graphs to facilitate downstream applications, such as finding the most
similar chemical compounds similar to a query compound or Fewshot 3D Action
Recognition. Recently, some graph similarity computation models based on neural
networks have been proposed, which are either based on graph-level interaction
or node-level comparison. However, when the number of nodes in the graph
increases, it will inevitably bring about reduced representation ability or
high computation cost. Motivated by this observation, we propose a graph
partitioning and graph neural network-based model, called PSimGNN, to
effectively resolve this issue. Specifically, each of the input graphs is
partitioned into a set of subgraphs to extract the local structural features
directly. Next, a novel graph neural network with an attention mechanism is
designed to map each subgraph into an embedding vector. Some of these subgraph
pairs are automatically selected for node-level comparison to supplement the
subgraph-level embedding with fine-grained information. Finally, coarse-grained
interaction information among subgraphs and fine-grained comparison information
among nodes in different subgraphs are integrated to predict the final
similarity score. Experimental results on graph datasets with different graph
sizes demonstrate that PSimGNN outperforms state-of-the-art methods in graph
similarity computation tasks using approximate Graph Edit Distance (GED) as the
graph similarity metric
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling
With the advancement of sensing technology, multivariate time series
classification (MTSC) has recently received considerable attention. Existing
deep learning-based MTSC techniques, which mostly rely on convolutional or
recurrent neural networks, are primarily concerned with the temporal dependency
of single time series. As a result, they struggle to express pairwise
dependencies among multivariate variables directly. Furthermore, current
spatial-temporal modeling (e.g., graph classification) methodologies based on
Graph Neural Networks (GNNs) are inherently flat and cannot aggregate hub data
in a hierarchical manner. To address these limitations, we propose a novel
graph pooling-based framework MTPool to obtain the expressive global
representation of MTS. We first convert MTS slices to graphs by utilizing
interactions of variables via graph structure learning module and attain the
spatial-temporal graph node features via temporal convolutional module. To get
global graph-level representation, we design an "encoder-decoder" based
variational graph pooling module for creating adaptive centroids for cluster
assignments. Then we combine GNNs and our proposed variational graph pooling
layers for joint graph representation learning and graph coarsening, after
which the graph is progressively coarsened to one node. At last, a
differentiable classifier takes this coarsened representation to get the final
predicted class. Experiments on ten benchmark datasets exhibit MTPool
outperforms state-of-the-art strategies in the MTSC task