8,940 research outputs found
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Many interesting problems in machine learning are being revisited with new
deep learning tools. For graph-based semisupervised learning, a recent
important development is graph convolutional networks (GCNs), which nicely
integrate local vertex features and graph topology in the convolutional layers.
Although the GCN model compares favorably with other state-of-the-art methods,
its mechanisms are not clear and it still requires a considerable amount of
labeled data for validation and model selection. In this paper, we develop
deeper insights into the GCN model and address its fundamental limits. First,
we show that the graph convolution of the GCN model is actually a special form
of Laplacian smoothing, which is the key reason why GCNs work, but it also
brings potential concerns of over-smoothing with many convolutional layers.
Second, to overcome the limits of the GCN model with shallow architectures, we
propose both co-training and self-training approaches to train GCNs. Our
approaches significantly improve GCNs in learning with very few labels, and
exempt them from requiring additional labels for validation. Extensive
experiments on benchmarks have verified our theory and proposals.Comment: AAAI-2018 Oral Presentatio
Bilinear Graph Neural Network with Neighbor Interactions
Graph Neural Network (GNN) is a powerful model to learn representations and
make predictions on graph data. Existing efforts on GNN have largely defined
the graph convolution as a weighted sum of the features of the connected nodes
to form the representation of the target node. Nevertheless, the operation of
weighted sum assumes the neighbor nodes are independent of each other, and
ignores the possible interactions between them. When such interactions exist,
such as the co-occurrence of two neighbor nodes is a strong signal of the
target node's characteristics, existing GNN models may fail to capture the
signal. In this work, we argue the importance of modeling the interactions
between neighbor nodes in GNN. We propose a new graph convolution operator,
which augments the weighted sum with pairwise interactions of the
representations of neighbor nodes. We term this framework as Bilinear Graph
Neural Network (BGNN), which improves GNN representation ability with bilinear
interactions between neighbor nodes. In particular, we specify two BGNN models
named BGCN and BGAT, based on the well-known GCN and GAT, respectively.
Empirical results on three public benchmarks of semi-supervised node
classification verify the effectiveness of BGNN -- BGCN (BGAT) outperforms GCN
(GAT) by 1.6% (1.5%) in classification accuracy.Codes are available at:
https://github.com/zhuhm1996/bgnn.Comment: Accepted by IJCAI 2020. SOLE copyright holder is IJCAI (International
Joint Conferences on Artificial Intelligence), all rights reserve
Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs
We propose a simple and generic layer formulation that extends the properties
of convolutional layers to any domain that can be described by a graph. Namely,
we use the support of its adjacency matrix to design learnable weight sharing
filters able to exploit the underlying structure of signals in the same fashion
as for images. The proposed formulation makes it possible to learn the weights
of the filter as well as a scheme that controls how they are shared across the
graph. We perform validation experiments with image datasets and show that
these filters offer performances comparable with convolutional ones.Comment: To appear in 2017, 5th IEEE Global Conference on Signal and
Information Processing, 5 pages, 3 figures, 3 table
Hashing for Similarity Search: A Survey
Similarity search (nearest neighbor search) is a problem of pursuing the data
items whose distances to a query item are the smallest from a large database.
Various methods have been developed to address this problem, and recently a lot
of efforts have been devoted to approximate search. In this paper, we present a
survey on one of the main solutions, hashing, which has been widely studied
since the pioneering work locality sensitive hashing. We divide the hashing
algorithms two main categories: locality sensitive hashing, which designs hash
functions without exploring the data distribution and learning to hash, which
learns hash functions according the data distribution, and review them from
various aspects, including hash function design and distance measure and search
scheme in the hash coding space
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty
With the rapid expansion of mobile phone networks in developing countries,
large-scale graph machine learning has gained sudden relevance in the study of
global poverty. Recent applications range from humanitarian response and
poverty estimation to urban planning and epidemic containment. Yet the vast
majority of computational tools and algorithms used in these applications do
not account for the multi-view nature of social networks: people are related in
myriad ways, but most graph learning models treat relations as binary. In this
paper, we develop a graph-based convolutional network for learning on
multi-view networks. We show that this method outperforms state-of-the-art
semi-supervised learning algorithms on three different prediction tasks using
mobile phone datasets from three different developing countries. We also show
that, while designed specifically for use in poverty research, the algorithm
also outperforms existing benchmarks on a broader set of learning tasks on
multi-view networks, including node labelling in citation networks
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