107 research outputs found
Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power
The ability of graph neural networks (GNNs) to count certain graph
substructures, especially cycles, is important for the success of GNNs on a
wide range of tasks. It has been recently used as a popular metric for
evaluating the expressive power of GNNs. Many of the proposed GNN models with
provable cycle counting power are based on subgraph GNNs, i.e., extracting a
bag of subgraphs from the input graph, generating representations for each
subgraph, and using them to augment the representation of the input graph.
However, those methods require heavy preprocessing, and suffer from high time
and memory costs. In this paper, we overcome the aforementioned limitations of
subgraph GNNs by proposing a novel class of GNNs -- -Distance-Restricted
FWL(2) GNNs, or -DRFWL(2) GNNs. -DRFWL(2) GNNs use node pairs whose
mutual distances are at most as the units for message passing to balance
the expressive power and complexity. By performing message passing among
distance-restricted node pairs in the original graph, -DRFWL(2) GNNs avoid
the expensive subgraph extraction operations in subgraph GNNs, making both the
time and space complexity lower. We theoretically show that the discriminative
power of -DRFWL(2) GNNs strictly increases as increases. More
importantly, -DRFWL(2) GNNs have provably strong cycle counting power even
with : they can count all 3, 4, 5, 6-cycles. Since 6-cycles (e.g., benzene
rings) are ubiquitous in organic molecules, being able to detect and count them
is crucial for achieving robust and generalizable performance on molecular
tasks. Experiments on both synthetic datasets and molecular datasets verify our
theory. To the best of our knowledge, our model is the most efficient GNN model
to date (both theoretically and empirically) that can count up to 6-cycles
SG-Net: Syntax-Guided Machine Reading Comprehension
For machine reading comprehension, the capacity of effectively modeling the
linguistic knowledge from the detail-riddled and lengthy passages and getting
ride of the noises is essential to improve its performance. Traditional
attentive models attend to all words without explicit constraint, which results
in inaccurate concentration on some dispensable words. In this work, we propose
using syntax to guide the text modeling by incorporating explicit syntactic
constraints into attention mechanism for better linguistically motivated word
representations. In detail, for self-attention network (SAN) sponsored
Transformer-based encoder, we introduce syntactic dependency of interest (SDOI)
design into the SAN to form an SDOI-SAN with syntax-guided self-attention.
Syntax-guided network (SG-Net) is then composed of this extra SDOI-SAN and the
SAN from the original Transformer encoder through a dual contextual
architecture for better linguistics inspired representation. To verify its
effectiveness, the proposed SG-Net is applied to typical pre-trained language
model BERT which is right based on a Transformer encoder. Extensive experiments
on popular benchmarks including SQuAD 2.0 and RACE show that the proposed
SG-Net design helps achieve substantial performance improvement over strong
baselines.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020
Vibration and instability of a fluid-conveying nanotube resting on elastic foundation subjected to a magnetic field
Using the nonlocal Euler-Bernouli beam model, this paper is carried out to investigate the vibrations and instability of a single-walled carbon nanotube (SWCNT) conveying fluid subjected to a longitudinal magnetic field. The nanobeam with clamped-clamped boundary conditions lies on the Pasternak foundation. Hamilton’s principle is applied to derive the fluid-structure interaction (FSI) governing equation and the corresponding boundary conditions. In the solution part the differential transformation method (DTM) is used to solve the differential equations of motion. The influences of nonlocal parameter, longitudinal magnetic field, Pasternak foundation on the critical divergence velocity of the nanotubes is studied
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