Community Detection algorithms are used to detect densely connected
components in complex networks and reveal underlying relationships among
components. As a special type of networks, spatial networks are usually
generated by the connections among geographic regions. Identifying the spatial
network communities can help reveal the spatial interaction patterns,
understand the hidden regional structures and support regional development
decision-making. Given the recent development of Graph Convolutional Networks
(GCN) and its powerful performance in identifying multi-scale spatial
interactions, we proposed an unsupervised GCN-based community detection method
"region2vec" on spatial networks. Our method first generates node embeddings
for regions that share common attributes and have intense spatial interactions,
and then applies clustering algorithms to detect communities based on their
embedding similarity and spatial adjacency. Experimental results show that
while existing methods trade off either attribute similarities or spatial
interactions for one another, "region2vec" maintains a great balance between
both and performs the best when one wants to maximize both attribute
similarities and spatial interactions within communities.

该研究提出了一个名为 “region2vec” 的无监督的基于 GCN 的社区检测方法，通过生成区域节点嵌入并应用聚类算法检测出具有相似嵌入和空间邻接的社区，以平衡属性相似性与空间相互作用在社区内的表现。实验结果表明该方法在属性和空间上均能得到良好的性能表现。

基于节点属性和空间交互的图嵌入方法用于空间网络社区发现

Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions

It is often stated that human languages, as other biological systems, are
shaped by cost-cutting pressures but, to what extent? Attempts to quantify the
degree of optimality of languages by means of an optimality score have been
scarce and focused mostly on English. Here we recast the problem of the
optimality of the word order of a sentence as an optimization problem on a
spatial network where the vertices are words, arcs indicate syntactic
dependencies and the space is defined by the linear order of the words in the
sentence. We introduce a new score to quantify the cognitive pressure to reduce
the distance between linked words in a sentence. The analysis of sentences from
93 languages representing 19 linguistic families reveals that half of languages
are optimized to a 70% or more. The score indicates that distances are not
significantly reduced in a few languages and confirms two theoretical
predictions, i.e. that longer sentences are more optimized and that distances
are more likely to be longer than expected by chance in short sentences. We
present a new hierarchical ranking of languages by their degree of
optimization. The new score has implications for various fields of language
research (dependency linguistics, typology, historical linguistics, clinical
linguistics and cognitive science). Finally, the principles behind the design
of the score have implications for network science.

通过空间网络将句子中单词的语法依赖关系表示为优化问题，引入新的评分方法来量化减少单词距离的认知压力，研究 93 种语言的句子，发现这种方法能准确评估大约一半的语言的优化水平，并对各个领域的语言研究提供了启示，特别是对网络科学有重要意义。

句法依赖距离的优化性

The optimality of syntactic dependency distances

We propose a technique that propagates information forward through video
data. The method is conceptually simple and can be applied to tasks that
require the propagation of structured information, such as semantic labels,
based on video content. We propose a 'Video Propagation Network' that processes
video frames in an adaptive manner. The model is applied online: it propagates
information forward without the need to access future frames. In particular we
combine two components, a temporal bilateral network for dense and video
adaptive filtering, followed by a spatial network to refine features and
increased flexibility. We present experiments on video object segmentation and
semantic video segmentation and show increased performance comparing to the
best previous task-specific methods, while having favorable runtime.
Additionally we demonstrate our approach on an example regression task of color
propagation in a grayscale video.

该研究提出了使用视频传播网络进行语义分割的方法，该方法使用时不需要访问未来帧，并且与基于任务的最佳方法相比具有更高的性能。