12,495 research outputs found
Adaptive Synchronization of Complex Dynamical Networks with State Predictor
This paper addresses the adaptive synchronization of complex dynamical networks with nonlinear dynamics. Based on the Lyapunov method, it is shown that the network can synchronize to the synchronous state by introducing local adaptive strategy to the coupling strengths. Moreover, it is also proved that the convergence speed of complex dynamical networks can be increased via designing a state predictor. Finally, some numerical simulations are worked out to illustrate the analytical results
Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNNs) as an emerging technique have
shown superior capacity of dealing with heterogeneous information network
(HIN). However, most HGNNs follow a semi-supervised learning manner, which
notably limits their wide use in reality since labels are usually scarce in
real applications. Recently, contrastive learning, a self-supervised method,
becomes one of the most exciting learning paradigms and shows great potential
when there are no labels. In this paper, we study the problem of
self-supervised HGNNs and propose a novel co-contrastive learning mechanism for
HGNNs, named HeCo. Different from traditional contrastive learning which only
focuses on contrasting positive and negative samples, HeCo employs cross-view
contrastive mechanism. Specifically, two views of a HIN (network schema and
meta-path views) are proposed to learn node embeddings, so as to capture both
of local and high-order structures simultaneously. Then the cross-view
contrastive learning, as well as a view mask mechanism, is proposed, which is
able to extract the positive and negative embeddings from two views. This
enables the two views to collaboratively supervise each other and finally learn
high-level node embeddings. Moreover, to further boost the performance of HeCo,
two additional methods are designed to generate harder negative samples with
high quality. Besides the invariant factors, view-specific factors
complementally provide the diverse structure information between different
nodes, which also should be contained into the final embeddings. Therefore, we
need to further explore each view independently and propose a modified model,
called HeCo++. Specifically, HeCo++ conducts hierarchical contrastive learning,
including cross-view and intra-view contrasts, which aims to enhance the mining
of respective structures.Comment: This paper has been accepted by TKDE as a regular paper. arXiv admin
note: substantial text overlap with arXiv:2105.0911
Persistent Ballistic Entanglement Spreading with Optimal Control in Quantum Spin Chains
Entanglement propagation provides a key routine to understand quantum
many-body dynamics in and out of equilibrium. In this work, we uncover that the
``variational entanglement-enhancing'' field (VEEF) robustly induces a
persistent ballistic spreading of entanglement in quantum spin chains. The VEEF
is time dependent, and is optimally controlled to maximize the bipartite
entanglement entropy (EE) of the final state. Such a linear growth persists
till the EE reaches the genuine saturation with the total number of spins. The EE
satisfies for the time , with the
velocity. These results are in sharp contrast with the behaviors without VEEF,
where the EE generally approaches a sub-saturation known as the Page value
in the long-time limit, and the
entanglement growth deviates from being linear before the Page value is
reached. The dependence between the velocity and interactions is explored, with
, , and for the spin chains with Ising, XY, and
Heisenberg interactions, respectively. We further show that the nonlinear
growth of EE emerges with the presence of long-range interactions.Comment: 5 pages, 4 figure
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