Training labels for graph embedding algorithms could be costly to obtain in
many practical scenarios. Active learning (AL) algorithms are very helpful to
obtain the most useful labels for training while keeping the total number of
label queries under a certain budget. The existing Active Graph Embedding
framework proposes to use centrality score, density score, and entropy score to
evaluate the value of unlabeled nodes, and it has been shown to be capable of
bringing some improvement to the node classification tasks of Graph
Convolutional Networks. However, when evaluating the importance of unlabeled
nodes, it fails to consider the influence of existing labeled nodes on the
value of unlabeled nodes. In other words, given the same unlabeled node, the
computed informative score is always the same and is agnostic to the labeled
node set. With the aim to address this limitation, in this work, we introduce 3
dissimilarity-based information scores for active learning: feature
dissimilarity score (FDS), structure dissimilarity score (SDS), and embedding
dissimilarity score (EDS). We find out that those three scores are able to take
the influence of the labeled set on the value of unlabeled candidates into
consideration, boosting our AL performance. According to experiments, our newly
proposed scores boost the classification accuracy by 2.1% on average and are
capable of generalizing to different Graph Neural Network architectures.

本文提出了基于相异性的信息评分方法，包括特征差异评分、结构差异评分和嵌入差异评分，能够考虑标记节点集对未标记节点价值的影响，有效提高主动学习的性能，实验结果表明，本文提出的评分方法平均提高了 2.1% 的分类准确率，且适用于不同图神经网络结构。

异构节点提高图形主动学习

Dissimilar Nodes Improve Graph Active Learning

Hierarchical clustering is a recursive partitioning of a dataset into
clusters at an increasingly finer granularity. Motivated by the fact that most
work on hierarchical clustering was based on providing algorithms, rather than
optimizing a specific objective, Dasgupta framed similarity-based hierarchical
clustering as a combinatorial optimization problem, where a `good' hierarchical
clustering is one that minimizes some cost function. He showed that this cost
function has certain desirable properties.
We take an axiomatic approach to defining `good' objective functions for both
similarity and dissimilarity-based hierarchical clustering. We characterize a
set of "admissible" objective functions (that includes Dasgupta's one) that
have the property that when the input admits a `natural' hierarchical
clustering, it has an optimal value.
Equipped with a suitable objective function, we analyze the performance of
practical algorithms, as well as develop better algorithms. For
similarity-based hierarchical clustering, Dasgupta showed that the divisive
sparsest-cut approach achieves an $O(\log^{3/2} n)$-approximation. We give a
refined analysis of the algorithm and show that it in fact achieves an
$O(\sqrt{\log n})$-approx. (Charikar and Chatziafratis independently proved
that it is a $O(\sqrt{\log n})$-approx.). This improves upon the LP-based
$O(\log n)$-approx. of Roy and Pokutta. For dissimilarity-based hierarchical
clustering, we show that the classic average-linkage algorithm gives a factor 2
approx., and provide a simple and better algorithm that gives a factor 3/2
approx..
Finally, we consider `beyond-worst-case' scenario through a generalisation of
the stochastic block model for hierarchical clustering. We show that Dasgupta's
cost function has desirable properties for these inputs and we provide a simple
1 + o(1)-approximation in this setting.

本篇论文研究层次聚类的优化问题，通过提出一些可靠的任务目标函数并对实际算法进行分析，提供了更优的算法以及性能表现。