2 research outputs found
Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks
Brain networks have received considerable attention given the critical
significance for understanding human brain organization, for investigating
neurological disorders and for clinical diagnostic applications. Structural
brain network (e.g. DTI) and functional brain network (e.g. fMRI) are the
primary networks of interest. Most existing works in brain network analysis
focus on either structural or functional connectivity, which cannot leverage
the complementary information from each other. Although multi-view learning
methods have been proposed to learn from both networks (or views), these
methods aim to reach a consensus among multiple views, and thus distinct
intrinsic properties of each view may be ignored. How to jointly learn
representations from structural and functional brain networks while preserving
their inherent properties is a critical problem. In this paper, we propose a
framework of Siamese community-preserving graph convolutional network (SCP-GCN)
to learn the structural and functional joint embedding of brain networks.
Specifically, we use graph convolutions to learn the structural and functional
joint embedding, where the graph structure is defined with structural
connectivity and node features are from the functional connectivity. Moreover,
we propose to preserve the community structure of brain networks in the graph
convolutions by considering the intra-community and inter-community properties
in the learning process. Furthermore, we use Siamese architecture which models
the pair-wise similarity learning to guide the learning process. To evaluate
the proposed approach, we conduct extensive experiments on two real brain
network datasets. The experimental results demonstrate the superior performance
of the proposed approach in structural and functional joint embedding for
neurological disorder analysis, indicating its promising value for clinical
applications
CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning
Recent years have witnessed the emergence and flourishing of hierarchical
graph pooling neural networks (HGPNNs) which are effective graph representation
learning approaches for graph level tasks such as graph classification.
However, current HGPNNs do not take full advantage of the graph's intrinsic
structures (e.g., community structure). Moreover, the pooling operations in
existing HGPNNs are difficult to be interpreted. In this paper, we propose a
new interpretable graph pooling framework - CommPOOL, that can capture and
preserve the hierarchical community structure of graphs in the graph
representation learning process. Specifically, the proposed community pooling
mechanism in CommPOOL utilizes an unsupervised approach for capturing the
inherent community structure of graphs in an interpretable manner. CommPOOL is
a general and flexible framework for hierarchical graph representation learning
that can further facilitate various graph-level tasks. Evaluations on five
public benchmark datasets and one synthetic dataset demonstrate the superior
performance of CommPOOL in graph representation learning for graph
classification compared to the state-of-the-art baseline methods, and its
effectiveness in capturing and preserving the community structure of graphs