Deep graph clustering has recently received significant attention due to its
ability to enhance the representation learning capabilities of models in
unsupervised scenarios. Nevertheless, deep clustering for temporal graphs,
which could capture crucial dynamic interaction information, has not been fully
explored. It means that in many clustering-oriented real-world scenarios,
temporal graphs can only be processed as static graphs. This not only causes
the loss of dynamic information but also triggers huge computational
consumption. To solve the problem, we propose a general framework for deep
Temporal Graph Clustering called TGC, which adjusts deep clustering techniques
(clustering assignment distribution and adjacency matrix reconstruction) to
suit the interaction sequence-based batch-processing pattern of temporal
graphs. In addition, we discuss differences between temporal graph clustering
and existing static graph clustering from several levels. To verify the
superiority of the proposed framework TGC, we conduct extensive experiments.
The experimental results show that temporal graph clustering enables more
flexibility in finding a balance between time and space requirements, and our
framework can effectively improve the performance of existing temporal graph
learning methods. Our code and supplementary material will be released after
publication.

提出了基于交互序列的批处理模式的 deep Temporal Graph Clustering (TGC) 框架，通过调整聚类分配和邻接矩阵重建技术来适应时间图的互动动态。实验结果表明，TGC 框架可以有效提高现有临时图学习方法的性能。

深度时变图聚类

Deep Temporal Graph Clustering

Learning feature interactions is the key to success for the large-scale CTR
prediction in Ads ranking and recommender systems. In industry, deep neural
network-based models are widely adopted for modeling such problems. Researchers
proposed various neural network architectures for searching and modeling the
feature interactions in an end-to-end fashion. However, most methods only learn
static feature interactions and have not fully leveraged deep CTR models'
representation capacity. In this paper, we propose a new model: DynInt. By
extending Polynomial-Interaction-Network (PIN), which learns higher-order
interactions recursively to be dynamic and data-dependent, DynInt further
derived two modes for modeling dynamic higher-order interactions: dynamic
activation and dynamic parameter. In dynamic activation mode, we adaptively
adjust the strength of learned interactions by instance-aware activation gating
networks. In dynamic parameter mode, we re-parameterize the parameters by
different formulations and dynamically generate the parameters by
instance-aware parameter generation networks. Through instance-aware gating
mechanism and dynamic parameter generation, we enable the PIN to model dynamic
interaction for potential industry applications. We implement the proposed
model and evaluate the model performance on real-world datasets. Extensive
experiment results demonstrate the efficiency and effectiveness of DynInt over
state-of-the-art models.

本文提出了一种新型神经网络：DynInt，通过动态交互模式，实现了更好的 CTR 预测，实验结果表明 DynInt 模型在真实世界数据集上比现有模型更高效、更有效。

DynInt: 大规模点击率预测的动态交互建模

DynInt: Dynamic Interaction Modeling for Large-scale Click-Through Rate Prediction

Many recommendation algorithms rely on user data to generate recommendations.
However, these recommendations also affect the data obtained from future users.
This work aims to understand the effects of this dynamic interaction. We
propose a simple model where users with heterogeneous preferences arrive over
time. Based on this model, we prove that naive estimators, i.e. those which
ignore this feedback loop, are not consistent. We show that consistent
estimators are efficient in the presence of myopic agents. Our results are
validated using extensive simulations.

该研究旨在了解推荐算法对未来用户数据的影响，通过提出一个简单模型，表明忽略这个反馈循环将导致非一致性评估器，因此验证了我们的结论。