Existing graph clustering networks heavily rely on a predefined graph and may
fail if the initial graph is of low quality. To tackle this issue, we propose a
novel graph augmentation clustering network capable of adaptively enhancing the
initial graph to achieve better clustering performance. Specifically, we first
integrate the node attribute and topology structure information to learn the
latent feature representation. Then, we explore the local geometric structure
information on the embedding space to construct an adjacency graph and
subsequently develop an adaptive graph augmentation architecture to fuse that
graph with the initial one dynamically. Finally, we minimize the Jeffreys
divergence between multiple derived distributions to conduct network training
in an unsupervised fashion. Extensive experiments on six commonly used
benchmark datasets demonstrate that the proposed method consistently
outperforms several state-of-the-art approaches. In particular, our method
improves the ARI by more than 9.39\% over the best baseline on DBLP. The source
codes and data have been submitted to the appendix.

该研究提出了一种新颖的图增强聚类网络，通过自适应增强初始图的方法，能够实现更好的聚类性能，实验结果表明该方法在六个基准数据集上的表现优于现有的几种先进方法。

图增强聚类网络

Graph Augmentation Clustering Network

Social and information networking activities such as on Facebook, Twitter,
WeChat, and Weibo have become an indispensable part of our everyday life, where
we can easily access friends' behaviors and are in turn influenced by them.
Consequently, an effective social influence prediction for each user is
critical for a variety of applications such as online recommendation and
advertising.
Conventional social influence prediction approaches typically design various
hand-crafted rules to extract user- and network-specific features. However,
their effectiveness heavily relies on the knowledge of domain experts. As a
result, it is usually difficult to generalize them into different domains.
Inspired by the recent success of deep neural networks in a wide range of
computing applications, we design an end-to-end framework, DeepInf, to learn
users' latent feature representation for predicting social influence. In
general, DeepInf takes a user's local network as the input to a graph neural
network for learning her latent social representation. We design strategies to
incorporate both network structures and user-specific features into
convolutional neural and attention networks. Extensive experiments on Open
Academic Graph, Twitter, Weibo, and Digg, representing different types of
social and information networks, demonstrate that the proposed end-to-end
model, DeepInf, significantly outperforms traditional feature engineering-based
approaches, suggesting the effectiveness of representation learning for social
applications.

作者设计了一个端到端的深度神经网络框架 DeepInf，用于学习用户的潜在特征表示以预测社交影响，并且在不同类型的社交和信息网络上展开了广泛的实验以证明该模型的显著优越性。

DeepInf: 基于深度学习的社交影响预测

DeepInf: Social Influence Prediction with Deep Learning

Domain adaptation is transfer learning which aims to generalize a learning
model across training and testing data with different distributions. Most
previous research tackle this problem in seeking a shared feature
representation between source and target domains while reducing the mismatch of
their data distributions. In this paper, we propose a close yet discriminative
domain adaptation method, namely CDDA, which generates a latent feature
representation with two interesting properties. First, the discrepancy between
the source and target domain, measured in terms of both marginal and
conditional probability distribution via Maximum Mean Discrepancy is minimized
so as to attract two domains close to each other. More importantly, we also
design a repulsive force term, which maximizes the distances between each label
dependent sub-domain to all others so as to drag different class dependent
sub-domains far away from each other and thereby increase the discriminative
power of the adapted domain. Moreover, given the fact that the underlying data
manifold could have complex geometric structure, we further propose the
constraints of label smoothness and geometric structure consistency for label
propagation. Extensive experiments are conducted on 36 cross-domain image
classification tasks over four public datasets. The comprehensive results show
that the proposed method consistently outperforms the state-of-the-art methods
with significant margins.

该论文提出了一种名称为 CDDA 的方法，通过最大平均差异度量源和目标域之间的差异，生成具有约束力的潜在特征表示，并提出标签平滑性和几何结构一致性的约束条件，进一步提高其适应领域的区分能力。