1,094 research outputs found
The semileptonic and radiative decays within the light-cone sum rules
The measured branching ratio of the meson semileptonic decay , which is based on the CLEO data taken at the
peak of resonance, disagrees with the traditional SVZ sum rules
analysis by about three times. In the paper, we show that this discrepancy can
be eliminated by applying the QCD light-cone sum rules (LCSR) approach to
calculate the transition form factors and .
After extrapolating the LCSR predictions of these TFFs to whole -region,
we obtain . Using the CKM matrix
element and the lifetime from the Particle Data Group, we obtain
and , which agree with the CLEO measurements within errors. We
also calculate the branching ratios of the two meson radiative processes
and obtain and , which also agree with the Belle measurements within errors. Thus we
think the LCSR approach is applicable for dealing with the meson decays.Comment: 12 pages, 7 figures, version to be published in EPJ
Behavior patterns of online users and the effect on information filtering
Understanding the structure and evolution of web-based user-object bipartite
networks is an important task since they play a fundamental role in online
information filtering. In this paper, we focus on investigating the patterns of
online users' behavior and the effect on recommendation process. Empirical
analysis on the e-commercial systems show that users have significant taste
diversity and their interests for niche items highly overlap. Additionally,
recommendation process are investigated on both the real networks and the
reshuffled networks in which real users' behavior patterns can be gradually
destroyed. Our results shows that the performance of personalized
recommendation methods is strongly related to the real network structure.
Detail study on each item shows that recommendation accuracy for hot items is
almost maximum and quite robust to the reshuffling process. However, niche
items cannot be accurately recommended after removing users' behavior patterns.
Our work also is meaningful in practical sense since it reveals an effective
direction to improve the accuracy and the robustness of the existing
recommender systems.Comment: 8 pages, 6 figure
Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes
Despite the recent successes of vanilla Graph Neural Networks (GNNs) on many
tasks, their foundation on pairwise interaction networks inherently limits
their capacity to discern latent higher-order interactions in complex systems.
To bridge this capability gap, we propose a novel approach exploiting the rich
mathematical theory of simplicial complexes (SCs) - a robust tool for modeling
higher-order interactions. Current SC-based GNNs are burdened by high
complexity and rigidity, and quantifying higher-order interaction strengths
remains challenging. Innovatively, we present a higher-order Flower-Petals (FP)
model, incorporating FP Laplacians into SCs. Further, we introduce a
Higher-order Graph Convolutional Network (HiGCN) grounded in FP Laplacians,
capable of discerning intrinsic features across varying topological scales. By
employing learnable graph filters, a parameter group within each FP Laplacian
domain, we can identify diverse patterns where the filters' weights serve as a
quantifiable measure of higher-order interaction strengths. The theoretical
underpinnings of HiGCN's advanced expressiveness are rigorously demonstrated.
Additionally, our empirical investigations reveal that the proposed model
accomplishes state-of-the-art (SOTA) performance on a range of graph tasks and
provides a scalable and flexible solution to explore higher-order interactions
in graphs
Coarse Graining for Synchronization in Directed Networks
Coarse graining model is a promising way to analyze and visualize large-scale
networks. The coarse-grained networks are required to preserve the same
statistical properties as well as the dynamic behaviors as the initial
networks. Some methods have been proposed and found effective in undirected
networks, while the study on coarse graining in directed networks lacks of
consideration. In this paper, we proposed a Topology-aware Coarse Graining
(TCG) method to coarse grain the directed networks. Performing the linear
stability analysis of synchronization and numerical simulation of the Kuramoto
model on four kinds of directed networks, including tree-like networks and
variants of Barab\'{a}si-Albert networks, Watts-Strogatz networks and
Erd\"{o}s-R\'{e}nyi networks, we find our method can effectively preserve the
network synchronizability.Comment: 9 pages, 7 figure
Uncovering missing links with cold ends
To evaluate the performance of prediction of missing links, the known data
are randomly divided into two parts, the training set and the probe set. We
argue that this straightforward and standard method may lead to terrible bias,
since in real biological and information networks, missing links are more
likely to be links connecting low-degree nodes. We therefore study how to
uncover missing links with low-degree nodes, namely links in the probe set are
of lower degree products than a random sampling. Experimental analysis on ten
local similarity indices and four disparate real networks reveals a surprising
result that the Leicht-Holme-Newman index [E. A. Leicht, P. Holme, and M. E. J.
Newman, Phys. Rev. E 73, 026120 (2006)] performs the best, although it was
known to be one of the worst indices if the probe set is a random sampling of
all links. We further propose an parameter-dependent index, which considerably
improves the prediction accuracy. Finally, we show the relevance of the
proposed index on three real sampling methods.Comment: 16 pages, 5 figures, 6 table
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