Graph neural networks (GNNs) have shown prominent performance on attributed
network embedding. However, existing efforts mainly focus on exploiting network
structures, while the exploitation of node attributes is rather limited as they
only serve as node features at the initial layer. This simple strategy impedes
the potential of node attributes in augmenting node connections, leading to
limited receptive field for inactive nodes with few or even no neighbors.
Furthermore, the training objectives (i.e., reconstructing network structures)
of most GNNs also do not include node attributes, although studies have shown
that reconstructing node attributes is beneficial. Thus, it is encouraging to
deeply involve node attributes in the key components of GNNs, including graph
convolution operations and training objectives. However, this is a nontrivial
task since an appropriate way of integration is required to maintain the merits
of GNNs. To bridge the gap, in this paper, we propose COllaborative graph
Neural Networks--CONN, a tailored GNN architecture for attribute network
embedding. It improves model capacity by 1) selectively diffusing messages from
neighboring nodes and involved attribute categories, and 2) jointly
reconstructing node-to-node and node-to-attribute-category interactions via
cross-correlation. Experiments on real-world networks demonstrate that CONN
excels state-of-the-art embedding algorithms with a great margin.

COllaborative graph Neural Networks (CONN) 是一种专门用于属性网络嵌入的 GNN 体系结构，通过选择性地传播来自相邻节点和相关属性类别的消息，并通过交叉相关联合重构节点之间和节点到属性类别之间的交互来提高模型能力。实验表明 CONN 在真实网络上表现出色，超过了现有的嵌入算法。

面向属性网络嵌入的协作图神经网络

Collaborative Graph Neural Networks for Attributed Network Embedding

Attributed network embedding aims to learn low-dimensional vector
representations for nodes in a network, where each node contains rich
attributes/features describing node content. Because network topology structure
and node attributes often exhibit high correlation, incorporating node
attribute proximity into network embedding is beneficial for learning good
vector representations. In reality, large-scale networks often have
incomplete/missing node content or linkages, yet existing attributed network
embedding algorithms all operate under the assumption that networks are
complete. Thus, their performance is vulnerable to missing data and suffers
from poor scalability.
In this paper, we propose a Scalable Incomplete Network Embedding (SINE)
algorithm for learning node representations from incomplete graphs. SINE
formulates a probabilistic learning framework that separately models pairs of
node-context and node-attribute relationships. Different from existing
attributed network embedding algorithms, SINE provides greater flexibility to
make the best of useful information and mitigate negative effects of missing
information on representation learning. A stochastic gradient descent based
online algorithm is derived to learn node representations, allowing SINE to
scale up to large-scale networks with high learning efficiency. We evaluate the
effectiveness and efficiency of SINE through extensive experiments on
real-world networks. Experimental results confirm that SINE outperforms
state-of-the-art baselines in various tasks, including node classification,
node clustering, and link prediction, under settings with missing links and
node attributes. SINE is also shown to be scalable and efficient on large-scale
networks with millions of nodes/edges and high-dimensional node features. The
source code of this paper is available at this https URL

本研究提出了一种基于概率学习框架的可伸缩的不完整网络嵌入算法，该算法能够单独建模节点上下文和节点属性的关系，增强使用有用信息和抵御缺失信息对表示学习的负面影响，且可应用在包含数百万节点 / 边和高维节点特征的大规模网络中。