36,102 research outputs found
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning
Graph pooling methods have been widely used on downsampling graphs, achieving
impressive results on multiple graph-level tasks like graph classification and
graph generation. An important line called node dropping pooling aims at
exploiting learnable scoring functions to drop nodes with comparatively lower
significance scores. However, existing node dropping methods suffer from two
limitations: (1) for each pooled node, these models struggle to capture
long-range dependencies since they mainly take GNNs as the backbones; (2)
pooling only the highest-scoring nodes tends to preserve similar nodes, thus
discarding the affluent information of low-scoring nodes. To address these
issues, we propose a Graph Transformer Pooling method termed GTPool, which
introduces Transformer to node dropping pooling to efficiently capture
long-range pairwise interactions and meanwhile sample nodes diversely.
Specifically, we design a scoring module based on the self-attention mechanism
that takes both global context and local context into consideration, measuring
the importance of nodes more comprehensively. GTPool further utilizes a
diversified sampling method named Roulette Wheel Sampling (RWS) that is able to
flexibly preserve nodes across different scoring intervals instead of only
higher scoring nodes. In this way, GTPool could effectively obtain long-range
information and select more representative nodes. Extensive experiments on 11
benchmark datasets demonstrate the superiority of GTPool over existing popular
graph pooling methods
Cell Attention Networks
Since their introduction, graph attention networks achieved outstanding
results in graph representation learning tasks. However, these networks
consider only pairwise relationships among nodes and then they are not able to
fully exploit higher-order interactions present in many real world data-sets.
In this paper, we introduce Cell Attention Networks (CANs), a neural
architecture operating on data defined over the vertices of a graph,
representing the graph as the 1-skeleton of a cell complex introduced to
capture higher order interactions. In particular, we exploit the lower and
upper neighborhoods, as encoded in the cell complex, to design two independent
masked self-attention mechanisms, thus generalizing the conventional graph
attention strategy. The approach used in CANs is hierarchical and it
incorporates the following steps: i) a lifting algorithm that learns {\it edge
features} from {\it node features}; ii) a cell attention mechanism to find the
optimal combination of edge features over both lower and upper neighbors; iii)
a hierarchical {\it edge pooling} mechanism to extract a compact meaningful set
of features. The experimental results show that CAN is a low complexity
strategy that compares favorably with state of the art results on graph-based
learning tasks.Comment: Preprint, under revie
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
Graph Neural Networks (GNN) have been shown to work effectively for modeling
graph structured data to solve tasks such as node classification, link
prediction and graph classification. There has been some recent progress in
defining the notion of pooling in graphs whereby the model tries to generate a
graph level representation by downsampling and summarizing the information
present in the nodes. Existing pooling methods either fail to effectively
capture the graph substructure or do not easily scale to large graphs. In this
work, we propose ASAP (Adaptive Structure Aware Pooling), a sparse and
differentiable pooling method that addresses the limitations of previous graph
pooling architectures. ASAP utilizes a novel self-attention network along with
a modified GNN formulation to capture the importance of each node in a given
graph. It also learns a sparse soft cluster assignment for nodes at each layer
to effectively pool the subgraphs to form the pooled graph. Through extensive
experiments on multiple datasets and theoretical analysis, we motivate our
choice of the components used in ASAP. Our experimental results show that
combining existing GNN architectures with ASAP leads to state-of-the-art
results on multiple graph classification benchmarks. ASAP has an average
improvement of 4%, compared to current sparse hierarchical state-of-the-art
method.Comment: The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI
2020
ParaFormer: Parallel Attention Transformer for Efficient Feature Matching
Heavy computation is a bottleneck limiting deep-learningbased feature
matching algorithms to be applied in many realtime applications. However,
existing lightweight networks optimized for Euclidean data cannot address
classical feature matching tasks, since sparse keypoint based descriptors are
expected to be matched. This paper tackles this problem and proposes two
concepts: 1) a novel parallel attention model entitled ParaFormer and 2) a
graph based U-Net architecture with attentional pooling. First, ParaFormer
fuses features and keypoint positions through the concept of amplitude and
phase, and integrates self- and cross-attention in a parallel manner which
achieves a win-win performance in terms of accuracy and efficiency. Second,
with U-Net architecture and proposed attentional pooling, the ParaFormer-U
variant significantly reduces computational complexity, and minimize
performance loss caused by downsampling. Sufficient experiments on various
applications, including homography estimation, pose estimation, and image
matching, demonstrate that ParaFormer achieves state-of-the-art performance
while maintaining high efficiency. The efficient ParaFormer-U variant achieves
comparable performance with less than 50% FLOPs of the existing attention-based
models.Comment: Have been accepted by AAAI 202
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
Event extraction is of practical utility in natural language processing. In
the real world, it is a common phenomenon that multiple events existing in the
same sentence, where extracting them are more difficult than extracting a
single event. Previous works on modeling the associations between events by
sequential modeling methods suffer a lot from the low efficiency in capturing
very long-range dependencies. In this paper, we propose a novel Jointly
Multiple Events Extraction (JMEE) framework to jointly extract multiple event
triggers and arguments by introducing syntactic shortcut arcs to enhance
information flow and attention-based graph convolution networks to model graph
information. The experiment results demonstrate that our proposed framework
achieves competitive results compared with state-of-the-art methods.Comment: accepted by EMNLP 201
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