5,540 research outputs found
Dating Documents using Graph Convolution Networks
Document date is essential for many important tasks, such as document
retrieval, summarization, event detection, etc. While existing approaches for
these tasks assume accurate knowledge of the document date, this is not always
available, especially for arbitrary documents from the Web. Document Dating is
a challenging problem which requires inference over the temporal structure of
the document. Prior document dating systems have largely relied on handcrafted
features while ignoring such document internal structures. In this paper, we
propose NeuralDater, a Graph Convolutional Network (GCN) based document dating
approach which jointly exploits syntactic and temporal graph structures of
document in a principled way. To the best of our knowledge, this is the first
application of deep learning for the problem of document dating. Through
extensive experiments on real-world datasets, we find that NeuralDater
significantly outperforms state-of-the-art baseline by 19% absolute (45%
relative) accuracy points.Comment: Accepted at ACL 201
Tensor graph convolutional neural network
In this paper, we propose a novel tensor graph convolutional neural network
(TGCNN) to conduct convolution on factorizable graphs, for which here two types
of problems are focused, one is sequential dynamic graphs and the other is
cross-attribute graphs. Especially, we propose a graph preserving layer to
memorize salient nodes of those factorized subgraphs, i.e. cross graph
convolution and graph pooling. For cross graph convolution, a parameterized
Kronecker sum operation is proposed to generate a conjunctive adjacency matrix
characterizing the relationship between every pair of nodes across two
subgraphs. Taking this operation, then general graph convolution may be
efficiently performed followed by the composition of small matrices, which thus
reduces high memory and computational burden. Encapsuling sequence graphs into
a recursive learning, the dynamics of graphs can be efficiently encoded as well
as the spatial layout of graphs. To validate the proposed TGCNN, experiments
are conducted on skeleton action datasets as well as matrix completion dataset.
The experiment results demonstrate that our method can achieve more competitive
performance with the state-of-the-art methods
Understanding Dynamic Scenes using Graph Convolution Networks
We present a novel Multi-Relational Graph Convolutional Network (MRGCN) based
framework to model on-road vehicle behaviors from a sequence of temporally
ordered frames as grabbed by a moving monocular camera. The input to MRGCN is a
multi-relational graph where the graph's nodes represent the active and passive
agents/objects in the scene, and the bidirectional edges that connect every
pair of nodes are encodings of their Spatio-temporal relations. We show that
this proposed explicit encoding and usage of an intermediate spatio-temporal
interaction graph to be well suited for our tasks over learning end-end
directly on a set of temporally ordered spatial relations. We also propose an
attention mechanism for MRGCNs that conditioned on the scene dynamically scores
the importance of information from different interaction types. The proposed
framework achieves significant performance gain over prior methods on
vehicle-behavior classification tasks on four datasets. We also show a seamless
transfer of learning to multiple datasets without resorting to fine-tuning.
Such behavior prediction methods find immediate relevance in a variety of
navigation tasks such as behavior planning, state estimation, and applications
relating to the detection of traffic violations over videos.Comment: To appear at IROS 202
Generalized Value Iteration Networks: Life Beyond Lattices
In this paper, we introduce a generalized value iteration network (GVIN),
which is an end-to-end neural network planning module. GVIN emulates the value
iteration algorithm by using a novel graph convolution operator, which enables
GVIN to learn and plan on irregular spatial graphs. We propose three novel
differentiable kernels as graph convolution operators and show that the
embedding based kernel achieves the best performance. We further propose
episodic Q-learning, an improvement upon traditional n-step Q-learning that
stabilizes training for networks that contain a planning module. Lastly, we
evaluate GVIN on planning problems in 2D mazes, irregular graphs, and
real-world street networks, showing that GVIN generalizes well for both
arbitrary graphs and unseen graphs of larger scale and outperforms a naive
generalization of VIN (discretizing a spatial graph into a 2D image).Comment: 14 pages, conferenc
Graph Convolution: A High-Order and Adaptive Approach
In this paper, we presented a novel convolutional neural network framework
for graph modeling, with the introduction of two new modules specially designed
for graph-structured data: the -th order convolution operator and the
adaptive filtering module. Importantly, our framework of High-order and
Adaptive Graph Convolutional Network (HA-GCN) is a general-purposed
architecture that fits various applications on both node and graph centrics, as
well as graph generative models. We conducted extensive experiments on
demonstrating the advantages of our framework. Particularly, our HA-GCN
outperforms the state-of-the-art models on node classification and molecule
property prediction tasks. It also generates 32% more real molecules on the
molecule generation task, both of which will significantly benefit real-world
applications such as material design and drug screening
Learning Depthwise Separable Graph Convolution from Data Manifold
Convolution Neural Network (CNN) has gained tremendous success in computer
vision tasks with its outstanding ability to capture the local latent features.
Recently, there has been an increasing interest in extending convolution
operations to the non-Euclidean geometry. Although various types of convolution
operations have been proposed for graphs or manifolds, their connections with
traditional convolution over grid-structured data are not well-understood. In
this paper, we show that depthwise separable convolution can be successfully
generalized for the unification of both graph-based and grid-based convolution
methods. Based on this insight we propose a novel Depthwise Separable Graph
Convolution (DSGC) approach which is compatible with the tradition convolution
network and subsumes existing convolution methods as special cases. It is
equipped with the combined strengths in model expressiveness, compatibility
(relatively small number of parameters), modularity and computational
efficiency in training. Extensive experiments show the outstanding performance
of DSGC in comparison with strong baselines on multi-domain benchmark datasets
Attributed Graph Clustering via Adaptive Graph Convolution
Attributed graph clustering is challenging as it requires joint modelling of
graph structures and node attributes. Recent progress on graph convolutional
networks has proved that graph convolution is effective in combining structural
and content information, and several recent methods based on it have achieved
promising clustering performance on some real attributed networks. However,
there is limited understanding of how graph convolution affects clustering
performance and how to properly use it to optimize performance for different
graphs. Existing methods essentially use graph convolution of a fixed and low
order that only takes into account neighbours within a few hops of each node,
which underutilizes node relations and ignores the diversity of graphs. In this
paper, we propose an adaptive graph convolution method for attributed graph
clustering that exploits high-order graph convolution to capture global cluster
structure and adaptively selects the appropriate order for different graphs. We
establish the validity of our method by theoretical analysis and extensive
experiments on benchmark datasets. Empirical results show that our method
compares favourably with state-of-the-art methods.Comment: IJCAI 201
Local Spectral Graph Convolution for Point Set Feature Learning
Feature learning on point clouds has shown great promise, with the
introduction of effective and generalizable deep learning frameworks such as
pointnet++. Thus far, however, point features have been abstracted in an
independent and isolated manner, ignoring the relative layout of neighboring
points as well as their features. In the present article, we propose to
overcome this limitation by using spectral graph convolution on a local graph,
combined with a novel graph pooling strategy. In our approach, graph
convolution is carried out on a nearest neighbor graph constructed from a
point's neighborhood, such that features are jointly learned. We replace the
standard max pooling step with a recursive clustering and pooling strategy,
devised to aggregate information from within clusters of nodes that are close
to one another in their spectral coordinates, leading to richer overall feature
descriptors. Through extensive experiments on diverse datasets, we show a
consistent demonstrable advantage for the tasks of both point set
classification and segmentation
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
\textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork (\textbf{GCN}) is
widely used in graph data learning tasks such as recommendation. However, when
facing a large graph, the graph convolution is very computationally expensive
thus is simplified in all existing GCNs, yet is seriously impaired due to the
oversimplification. To address this gap, we leverage the \textit{original graph
convolution} in GCN and propose a \textbf{L}ow-pass \textbf{C}ollaborative
\textbf{F}ilter (\textbf{LCF}) to make it applicable to the large graph. LCF is
designed to remove the noise caused by exposure and quantization in the
observed data, and it also reduces the complexity of graph convolution in an
unscathed way. Experiments show that LCF improves the effectiveness and
efficiency of graph convolution and our GCN outperforms existing GCNs
significantly. Codes are available on \url{https://github.com/Wenhui-Yu/LCFN}.Comment: ICML 2020 pape
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
Dependency trees help relation extraction models capture long-range relations
between words. However, existing dependency-based models either neglect crucial
information (e.g., negation) by pruning the dependency trees too aggressively,
or are computationally inefficient because it is difficult to parallelize over
different tree structures. We propose an extension of graph convolutional
networks that is tailored for relation extraction, which pools information over
arbitrary dependency structures efficiently in parallel. To incorporate
relevant information while maximally removing irrelevant content, we further
apply a novel pruning strategy to the input trees by keeping words immediately
around the shortest path between the two entities among which a relation might
hold. The resulting model achieves state-of-the-art performance on the
large-scale TACRED dataset, outperforming existing sequence and
dependency-based neural models. We also show through detailed analysis that
this model has complementary strengths to sequence models, and combining them
further improves the state of the art.Comment: EMNLP 2018. Code available at:
https://github.com/qipeng/gcn-over-pruned-tree
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