Entity alignment (EA) is a fundamental data integration task that identifies
equivalent entities between different knowledge graphs (KGs). Temporal
Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing
timestamps, which have received increasing attention. State-of-the-art
time-aware EA studies have suggested that the temporal information of TKGs
facilitates the performance of EA. However, existing studies have not
thoroughly exploited the advantages of temporal information in TKGs. Also, they
perform EA by pre-aligning entity pairs, which can be labor-intensive and thus
inefficient.
In this paper, we present DualMatch which effectively fuses the relational
and temporal information for EA. DualMatch transfers EA on TKGs into a weighted
graph matching problem. More specifically, DualMatch is equipped with an
unsupervised method, which achieves EA without necessitating seed alignment.
DualMatch has two steps: (i) encoding temporal and relational information into
embeddings separately using a novel label-free encoder, Dual-Encoder; and (ii)
fusing both information and transforming it into alignment using a novel
graph-matching-based decoder, GM-Decoder. DualMatch is able to perform EA on
TKGs with or without supervision, due to its capability of effectively
capturing temporal information. Extensive experiments on three real-world TKG
datasets offer the insight that DualMatch outperforms the state-of-the-art
methods in terms of H@1 by 2.4% - 10.7% and MRR by 1.7% - 7.6%, respectively.

本文介绍了一种名为 DualMatch 的方法，这种方法在实体对齐中有效地融合了关系和时间信息，并将其转化为一种基于加权图匹配的解码器。DualMatch 具有无监督方法和能够有效捕获时间信息等优点，能够在有监督和无监督的情况下执行。实验结果表明，Dual Match 在三个真实世界的时间知识图数据集上表现优于现有的方法。

面向时间知识图谱的无监督实体对齐

Unsupervised Entity Alignment for Temporal Knowledge Graphs

Person re-identification (re-id), an emerging problem in visual surveillance,
deals with maintaining entities of individuals whilst they traverse various
locations surveilled by a camera network. From a visual perspective re-id is
challenging due to significant changes in visual appearance of individuals in
cameras with different pose, illumination and calibration. Globally the
challenge arises from the need to maintain structurally consistent matches
among all the individual entities across different camera views. We propose
PRISM, a structured matching method to jointly account for these challenges. We
view the global problem as a weighted graph matching problem and estimate edge
weights by learning to predict them based on the co-occurrences of visual
patterns in the training examples. These co-occurrence based scores in turn
account for appearance changes by inferring likely and unlikely visual
co-occurrences appearing in training instances. We implement PRISM on single
shot and multi-shot scenarios. PRISM uniformly outperforms state-of-the-art in
terms of matching rate while being computationally efficient.

该研究提出了 PRISM，一种结构化匹配方法，用于应对人物重识别在视觉监控中的挑战。PRISM 将全局问题视为加权图匹配问题，并通过学习来预测边缘权重，并实现了 PRISM 并能够有效地应对各种挑战。

PRISM: 基于结构匹配的人员重识别

PRISM: Person Re-Identification via Structured Matching

In this paper, we consider the approximate weighted graph matching problem
and introduce stable and informative first and second order compatibility terms
suitable for inclusion into the popular integer quadratic program formulation.
Our approach relies on a rigorous analysis of stability of spectral signatures
based on the graph Laplacian. In the case of the first order term, we derive an
objective function that measures both the stability and informativeness of a
given spectral signature. By optimizing this objective, we design new spectral
node signatures tuned to a specific graph to be matched. We also introduce the
pairwise heat kernel distance as a stable second order compatibility term; we
justify its plausibility by showing that in a certain limiting case it
converges to the classical adjacency matrix-based second order compatibility
function. We have tested our approach on a set of synthetic graphs, the
widely-used CMU house sequence, and a set of real images. These experiments
show the superior performance of our first and second order compatibility terms
as compared with the commonly used ones.

本文提出适用于整数二次规划公式的稳定和信息量丰富的一阶和二阶兼容性术语，这是针对近似加权图匹配问题，基于图拉普拉斯的稳定性谱特征分析，包括一种测量给定特征值的稳定性和信息量的目标函数，以及作为稳定的二阶兼容性项的成对热核距离，并在合成图和实际图像上对其进行了测试并表明了其性能优越性。

图匹配的稳定和信息丰富的频谱特征

Stable and Informative Spectral Signatures for Graph Matching

We propose a convex-concave programming approach for the labeled weighted
graph matching problem. The convex-concave programming formulation is obtained
by rewriting the weighted graph matching problem as a least-square problem on
the set of permutation matrices and relaxing it to two different optimization
problems: a quadratic convex and a quadratic concave optimization problem on
the set of doubly stochastic matrices. The concave relaxation has the same
global minimum as the initial graph matching problem, but the search for its
global minimum is also a hard combinatorial problem. We therefore construct an
approximation of the concave problem solution by following a solution path of a
convex-concave problem obtained by linear interpolation of the convex and
concave formulations, starting from the convex relaxation. This method allows
to easily integrate the information on graph label similarities into the
optimization problem, and therefore to perform labeled weighted graph matching.
The algorithm is compared with some of the best performing graph matching
methods on four datasets: simulated graphs, QAPLib, retina vessel images and
handwritten chinese characters. In all cases, the results are competitive with
the state-of-the-art.

本文提出了一种凸凹规划方法，用于带标签的加权图匹配问题，并且提出的算法在四个数据集上的表现都优于现有算法。