Graph Neural Networks have recently become a prevailing paradigm for various high-impact graph analytical problems. Existing efforts can be mainly categorized as spectral-based and spatial-based methods. The major challenge for the former is to find an appropriate graph filter to distill discriminative information from input signals for learning. Recently, myriads of explorations are made to achieve better graph filters, e.g., Graph Convolutional Network (GCN), which leverages Chebyshev polynomial truncation to seek an approximation of graph filters and bridge these two families of methods. Nevertheless, it has been shown in recent studies that GCN and its variants are essentially employing fixed low-pass filters to perform information denoising. Thus their learning capability is rather limited and may over-smooth node representations at deeper layers. To tackle these problems, we develop a novel graph neural network framework AdaGNN with a well-designed adaptive frequency response filter. At its core, AdaGNN leverages a simple but elegant trainable filter that spans across multiple layers to capture the varying importance of different frequency components for node representation learning. The inherent differences among different feature channels are also well captured by the filter. As such, it empowers AdaGNN with stronger expressiveness and naturally alleviates the over-smoothing problem. We empirically validate the effectiveness of the proposed framework on various benchmark datasets. Theoretical analysis is also provided to show the superiority of the proposed AdaGNN. The open-source implementation of AdaGNN can be found here: https://github.com/yushundong/AdaGNN.
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ORIGIN: Non-Rigid Network Alignment
Network alignment is a fundamental task in many
high-impact applications. Most of the existing approaches either
explicitly or implicitly consider the alignment matrix as
a linear transformation to map one network to another, and
might overlook the complicated alignment relationship across
networks. On the other hand, node representation learning based
alignment methods are hampered by the incomparability among
the node representations of different networks. In this paper,
we propose a unified semi-supervised deep model (ORIGIN)
that simultaneously finds the non-rigid network alignment and
learns node representations in multiple networks in a mutually
beneficial way. The key idea is to learn node representations by
the effective graph convolutional networks, which subsequently
enable us to formulate network alignment as a point set alignment
problem. The proposed method offers two distinctive advantages.
First (node representations), unlike the existing graph convolutional
networks that aggregate the node information within
a single network, we can effectively aggregate the auxiliary
information from multiple sources, achieving far-reaching node
representations. Second (network alignment), guided by the highquality
node representations, our proposed non-rigid point set
alignment approach overcomes the bottleneck of the linear
transformation assumption. We conduct extensive experiments
that demonstrate the proposed non-rigid alignment method
is (1) effective, outperforming both the state-of-the-art linear
transformation-based methods and node representation based
methods, and (2) efficient, with a comparable computational
time between the proposed multi-network representation learning
component and its single-network counterpart.
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- NSF-PAR ID:
- 10159290
- Date Published:
- Journal Name:
- BigData
- Page Range / eLocation ID:
- 998 to 1007
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData47090.2019.9005663
