13,610 research outputs found
Modeling Relational Data via Latent Factor Blockmodel
In this paper we address the problem of modeling relational data, which
appear in many applications such as social network analysis, recommender
systems and bioinformatics. Previous studies either consider latent feature
based models but disregarding local structure in the network, or focus
exclusively on capturing local structure of objects based on latent blockmodels
without coupling with latent characteristics of objects. To combine the
benefits of the previous work, we propose a novel model that can simultaneously
incorporate the effect of latent features and covariates if any, as well as the
effect of latent structure that may exist in the data. To achieve this, we
model the relation graph as a function of both latent feature factors and
latent cluster memberships of objects to collectively discover globally
predictive intrinsic properties of objects and capture latent block structure
in the network to improve prediction performance. We also develop an
optimization transfer algorithm based on the generalized EM-style strategy to
learn the latent factors. We prove the efficacy of our proposed model through
the link prediction task and cluster analysis task, and extensive experiments
on the synthetic data and several real world datasets suggest that our proposed
LFBM model outperforms the other state of the art approaches in the evaluated
tasks.Comment: 10 pages, 12 figure
On the sine-Gordon--Thirring equivalence in the presence of a boundary
In this paper, the relationship between the sine-Gordon model with an
integrable boundary condition and the Thirring model with boundary is discussed
and the reflection -matrix for the massive Thirring model, which is related
to the physical boundary parameters of the sine-Gordon model, is given. The
relationship between the the boundary parameters and the two formal parameters
appearing in the work of Ghoshal and Zamolodchikov is discussed.Comment: 14 pages, Latex, to be published in Int. J. Mod. Phys. A. Two
references adde
Probabilistic Latent Tensor Factorization Model for Link Pattern Prediction in Multi-relational Networks
This paper aims at the problem of link pattern prediction in collections of
objects connected by multiple relation types, where each type may play a
distinct role. While common link analysis models are limited to single-type
link prediction, we attempt here to capture the correlations among different
relation types and reveal the impact of various relation types on performance
quality. For that, we define the overall relations between object pairs as a
\textit{link pattern} which consists in interaction pattern and connection
structure in the network, and then use tensor formalization to jointly model
and predict the link patterns, which we refer to as \textit{Link Pattern
Prediction} (LPP) problem. To address the issue, we propose a Probabilistic
Latent Tensor Factorization (PLTF) model by introducing another latent factor
for multiple relation types and furnish the Hierarchical Bayesian treatment of
the proposed probabilistic model to avoid overfitting for solving the LPP
problem. To learn the proposed model we develop an efficient Markov Chain Monte
Carlo sampling method. Extensive experiments are conducted on several real
world datasets and demonstrate significant improvements over several existing
state-of-the-art methods.Comment: 19pages, 5 figure
Transverse momentum resummation in soft-collinear effective theory
We present a universal formalism for transverse momentum resummation in the
view of soft-collinear effective theory (SCET), and establish the relation
between our SCET formula and the well known Collins-Soper-Sterman's pQCD
formula at the next-to-leading logarithmic order (NLLO). We also briefly
discuss the reformulation of joint resummation in SCET.Comment: 23 pages, 7 figures; version to appear in Phys. Rev.
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