Recent advances in the field of network embedding have shown that
low-dimensional network representation is playing a critical role in network
analysis. Most existing network embedding methods encode the local proximity of
a node, such as the first- and second-order proximities. While being efficient,
these methods are short of leveraging the global structural information between
nodes distant from each other. In addition, most existing methods learn
embeddings on one single fixed network, and thus cannot be generalized to
unseen nodes or networks without retraining. In this paper we present SPINE, a
method that can jointly capture the local proximity and proximities at any
distance, while being inductive to efficiently deal with unseen nodes or
networks. Extensive experimental results on benchmark datasets demonstrate the
superiority of the proposed framework over the state of the art.

SPINE 是一种具有归纳性的网络嵌入方法，可以同时捕捉节点之间的局部距离和任意距离的结构信息，并能够有效地处理未见节点。该方法在基准数据集上取得了超越现有技术的结果。

SPINE: 结构保持归纳网络嵌入

SPINE: Structural Identity Preserved Inductive Network Embedding

Learning distributed node representations in networks has been attracting
increasing attention recently due to its effectiveness in a variety of
applications. Existing approaches usually study networks with a single type of
proximity between nodes, which defines a single view of a network. However, in
reality there usually exists multiple types of proximities between nodes,
yielding networks with multiple views. This paper studies learning node
representations for networks with multiple views, which aims to infer robust
node representations across different views. We propose a multi-view
representation learning approach, which promotes the collaboration of different
views and lets them vote for the robust representations. During the voting
process, an attention mechanism is introduced, which enables each node to focus
on the most informative views. Experimental results on real-world networks show
that the proposed approach outperforms existing state-of-the-art approaches for
network representation learning with a single view and other competitive
approaches with multiple views.

本文研究多视角学习节点表示，提出了一种多视图表示学习方法，促进了不同视图之间的协作，并让它们为健壮表示投票，投票过程中引入了注意机制，实验结果表明该方法优于现有的单视图和竞争的多视图网络表示学习方法。

基于注意力机制的多视角网络表示学习协作框架

An Attention-based Collaboration Framework for Multi-View Network  Representation Learning

In many practical applications of clustering, the objects to be clustered
evolve over time, and a clustering result is desired at each time step. In such
applications, evolutionary clustering typically outperforms traditional static
clustering by producing clustering results that reflect long-term trends while
being robust to short-term variations. Several evolutionary clustering
algorithms have recently been proposed, often by adding a temporal smoothness
penalty to the cost function of a static clustering method. In this paper, we
introduce a different approach to evolutionary clustering by accurately
tracking the time-varying proximities between objects followed by static
clustering. We present an evolutionary clustering framework that adaptively
estimates the optimal smoothing parameter using shrinkage estimation, a
statistical approach that improves a naive estimate using additional
information. The proposed framework can be used to extend a variety of static
clustering algorithms, including hierarchical, k-means, and spectral
clustering, into evolutionary clustering algorithms. Experiments on synthetic
and real data sets indicate that the proposed framework outperforms static
clustering and existing evolutionary clustering algorithms in many scenarios.

本文提出了一种基于追踪对象间时间变化相似度并结合静态聚类的进化聚类方法，并使用缩减估计法自适应估计最优平滑参数，实验表明该方法在许多场景下优于静态聚类和已有的进化聚类方法。