Temporal point process as the stochastic process on a continuous domain of time is commonly used to model the asynchronous event sequence featuring occurrence timestamps. Thanks to the strong expressivity of deep neural networks, they are emerging as a promising choice for capturing the patterns in asynchronous sequences, in the context of temporal point process. In this paper, we first review recent research emphasis and difficulties in modeling asynchronous event sequences with deep temporal point process, which can be concluded into four fields: encoding of history sequence, formulation of conditional intensity function, relational discovery of events, and learning approaches for optimization. We introduce most of the recently proposed models by dismantling them into four parts and conduct experiments by re-modularizing the first three parts with the same learning strategy for a fair empirical evaluation. Besides, we extend the history encoders and conditional intensity function family and propose a Granger causality discovery framework for exploiting the relations among multi-types of events. Because the Granger causality can be represented by the Granger causality graph, discrete graph structure learning in the framework of Variational Inference is employed to reveal latent structures of the graph. Further experiments show that the proposed framework with latent graph discovery can both capture the relations and achieve an improved fitting and predicting performance.

Self-supervised graph representation learning has received increasing research interest recently, with generative and contrastive modeling being two dominant ways. Typically, generative learning first masks parts of each graph and then recovers the masked parts based on the encoding results of the corrupted graph. However, these methods only mask fixed parts of each graph and fail to train on all the nodes and edges, which hinders them from getting the most out of each graph. As a remedy, we propose a novel self-supervised strategy, dubbed DetCor, where we first randomly replace some nodes and edges with alternative ones and then pre-train GNNs to detect and correct the replaced ones from all the nodes and edges. Additionally, for graph-level learning, the vanilla contrastive framework cannot reflect the distinction between the in-batch negatives. To alleviate this issue, we propose RankGCL, which enables the contrastive framework to capture the similarity ranking information between graphs and shows special superiority in graph similarity-based practical tasks. DetCor and RankGCL together constitute a unified self-supervised framework, DiscoGNN, which matches or outperforms state-of-the-art strategies on multiple datasets from various domains. Also, DiscoGNN is a sample-efficient framework that can achieve better performance than competitive methods with much less pre-training data. We release the codes at: https://github.com/junxia97/DiscoGNN-ICDE.

Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all-encompassing Artificial Intelligence for General Creativity (AIGC). Notably, diffusion models, recognized as one of the paramount generative models, materialize human ideation into tangible instances across diverse domains, encompassing imagery, text, speech, biology, and healthcare. To provide advanced and comprehensive insights into diffusion, this survey comprehensively elucidates its developmental trajectory and future directions from three distinct angles: the fundamental formulation of diffusion, algorithmic enhancements, and the manifold applications of diffusion. Each layer is meticulously explored to offer a profound comprehension of its evolution. Structured and summarized approaches are presented here.

Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). Despite their greatacademicsuccess, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practicalindustrialapplications. One reason for such an academic-industry gap is the neighborhood-fetching latency incurred by data dependency in GNNs. To reduce their gaps, Graph Knowledge Distillation (GKD) is proposed, usually based on a standard teacher-student architecture, to distill knowledge from a large teacher GNN into a lightweight student GNN or MLP. However, we found in this paper that neither teachers nor GNNs are necessary for graph knowledge distillation. We propose aTeacher-FreeGraphSelf-Distillation(TGS) framework that does not require any teacher model or GNNs during both training and inference. More importantly, the proposed TGS framework is purely based on MLPs, where structural information is only implicitly used to guidedual knowledge self-distillationbetween the target node and its neighborhood. As a result, TGS enjoys the benefits of graph topology awareness in training but is free from data dependency in inference. Extensive experiments have shown that the performance of vanilla MLPs can be greatly improved with dual self-distillation, e.g., TGS improves over vanilla MLPs by 15.54% on average and outperforms state-of-the-art GKD algorithms on six real-world datasets. In terms of inference speed, TGS infers 75×-89× faster than existing GNNs and 16×-25× faster than classical inference acceleration methods.

Deep learning on graphs has recently achieved remarkable success on a variety of tasks, while such success relies heavily on the massive and carefully labeled data. However, precise annotations are generally very expensive and time-consuming. To address this problem, self-supervised learning (SSL) is emerging as a new paradigm for extracting informative knowledge through well-designed pretext tasks without relying on manual labels. In this survey, we extend the concept of SSL, which first emerged in the fields of computer vision and natural language processing, to present a timely and comprehensive review of existing SSL techniques for graph data. Specifically, we divide existing graph SSL methods into three categories: contrastive, generative, and predictive. More importantly, unlike other surveys that only provide a high-level description of published research, we present an additional mathematical summary of existing works in a unified framework. Furthermore, to facilitate methodological development and empirical comparisons, we also summarize the commonly used datasets, evaluation metrics, downstream tasks, open-source implementations, and experimental study of various algorithms. Finally, we discuss the technical challenges and potential future directions for improving graph self-supervised learning. Latest advances in graph SSL are summarized in a GitHub repositoryhttps://github.com/LirongWu/awesome-graph-self-supervised-learning.