60 research outputs found
MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions
Predicting interactions between structured entities lies at the core of
numerous tasks such as drug regimen and new material design. In recent years,
graph neural networks have become attractive. They represent structured
entities as graphs and then extract features from each individual graph using
graph convolution operations. However, these methods have some limitations: i)
their networks only extract features from a fix-sized subgraph structure (i.e.,
a fix-sized receptive field) of each node, and ignore features in substructures
of different sizes, and ii) features are extracted by considering each entity
independently, which may not effectively reflect the interaction between two
entities. To resolve these problems, we present MR-GNN, an end-to-end graph
neural network with the following features: i) it uses a multi-resolution based
architecture to extract node features from different neighborhoods of each
node, and, ii) it uses dual graph-state long short-term memory networks
(L-STMs) to summarize local features of each graph and extracts the interaction
features between pairwise graphs. Experiments conducted on real-world datasets
show that MR-GNN improves the prediction of state-of-the-art methods.Comment: Accepted by IJCAI 201
Aggregate node placement for maximizing network lifetime in sensor networks
Sensor networks have been receiving significant attention due to their potential applications in environmental monitoring and surveillance domains. In this paper, we consider the design issue of sensor networks by placing a few powerful aggregate nodes into a dense sensor network such that the network lifetime is significantly prolonged when performing data gathering. Specifically, given K aggregate nodes and a dense sensor network consisting of n sensors with K ≪ n, the problem is to place the K aggregate nodes into the network such that the lifetime of the resulting network is maximized, subject to the distortion constraints that both the maximum transmission range of an aggregate node and the maximum transmission delay between an aggregate node and its covered sensor are met. This problem is a joint optimization problem of aggregate node placement and the communication structure, which is NP-hard. In this paper, we first give a non-linear programming solution for it. We then devise a novel heuristic algorithm. We finally conduct experiments by simulation to evaluate the performance of the proposed algorithm in terms of network lifetime. The experimental results show that the proposed algorithm outperforms a commonly used uniform placement schema - equal distance placement schema significantly
Tackling Over-Smoothing for General Graph Convolutional Networks
Increasing the depth of GCN, which is expected to permit more expressivity,
is shown to incur performance detriment especially on node classification. The
main cause of this lies in over-smoothing. The over-smoothing issue drives the
output of GCN towards a space that contains limited distinguished information
among nodes, leading to poor expressivity. Several works on refining the
architecture of deep GCN have been proposed, but it is still unknown in theory
whether or not these refinements are able to relieve over-smoothing. In this
paper, we first theoretically analyze how general GCNs act with the increase in
depth, including generic GCN, GCN with bias, ResGCN, and APPNP. We find that
all these models are characterized by a universal process: all nodes converging
to a cuboid. Upon this theorem, we propose DropEdge to alleviate over-smoothing
by randomly removing a certain number of edges at each training epoch.
Theoretically, DropEdge either reduces the convergence speed of over-smoothing
or relieves the information loss caused by dimension collapse. Experimental
evaluations on simulated dataset have visualized the difference in
over-smoothing between different GCNs. Moreover, extensive experiments on
several real benchmarks support that DropEdge consistently improves the
performance on a variety of both shallow and deep GCNs.Comment: Submitted to TPAMI, 15 page
Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays
Chest X-rays is one of the most commonly available and affordable
radiological examinations in clinical practice. While detecting thoracic
diseases on chest X-rays is still a challenging task for machine intelligence,
due to 1) the highly varied appearance of lesion areas on X-rays from patients
of different thoracic disease and 2) the shortage of accurate pixel-level
annotations by radiologists for model training. Existing machine learning
methods are unable to deal with the challenge that thoracic diseases usually
happen in localized disease-specific areas. In this article, we propose a
weakly supervised deep learning framework equipped with squeeze-and-excitation
blocks, multi-map transfer, and max-min pooling for classifying thoracic
diseases as well as localizing suspicious lesion regions. The comprehensive
experiments and discussions are performed on the ChestX-ray14 dataset. Both
numerical and visual results have demonstrated the effectiveness of the
proposed model and its better performance against the state-of-the-art
pipelines.Comment: 10 pages. Accepted by the ACM BCB 201
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