Graph Neural Networks (GNNs) have become the backbone for a myriad of tasks pertaining to graphs and similar topological data structures. While many works have been established in domains related to node and graph classification/regression tasks, they mostly deal with a single task. Continual learning on graphs is largely unexplored and existing graph continual learning approaches are limited to the task-incremental learning scenarios. This paper proposes a graph continual learning strategy that combines the architecture-based and memory-based approaches. The structural learning strategy is driven by reinforcement learning, where a controller network is trained in such a way to determine an optimal number of nodes to be added/pruned from the base network when new tasks are observed, thus assuring sufficient network capacities. The parameter learning strategy is underpinned by the concept of Dark Experience replay method to cope with the catastrophic forgetting problem. Our approach is numerically validated with several graph continual learning benchmark problems in both task-incremental learning and class-incremental learning settings. Compared to recently published works, our approach demonstrates improved performance in both the settings. The implementation code can be found at \url{https://github.com/codexhammer/gcl}.
Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback
School of Computer Science and Technology, East China Normal University（东华师范大学计算机科学与技术学院）；Shanghai Innovation Institute（上海创新研究院）；School of Computer Science, Fudan University（复旦大学计算机科学学院）
Peking University（北京大学）；Beijing Institute of Big Data Research (BIBDR)（北京大数据研究院）
Most artificial intelligence models have limiting ability to solve new tasks faster, without forgetting previously acquired knowledge. The recently emerging paradigm of continual learning aims to solve this issue, in which the model learns various tasks in a sequential fashion. In this work, a novel approach for continual learning is proposed, which searches for the best neural architecture for each coming task via sophisticatedly designed reinforcement learning strategies. We name it as Reinforced Continual Learning. Our method not only has good performance on preventing catastrophic forgetti…
Department of Electrical Engineering, National Taiwan University（国家台湾大学电子工程系）；Department of Computer Science and Information Engineering, Chang Gung University（长庚大学计算机科学与信息工程系）
University of Science and Technology of China（中国科学技术大学）；CCCD Key Lab of Ministry of Culture and Tourism（文化和旅游部CCCD重点实验室）；National University of Singapore（新加坡国立大学）
Explainability is crucial for probing graph neural networks (GNNs), answering questions like "Why the GNN model makes a certain prediction?". Feature attribution is a prevalent technique of highlighting the explanatory subgraph in the input graph, which plausibly leads the GNN model to make its prediction. Various attribution methods exploit gradient-like or attention scores as the attributions of edges, then select the salient edges with top attribution scores as the explanation. However, most of these works make an untenable assumption - the selected edges are linearly independent - thus lea…
Beijing Institute of Technology（北京理工大学）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）
Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However, existing GE models are not practical in real-world applications since it overlooked the streaming nature of incoming data. To address this issue, we study the problem of continual graph representation learning which aims to continually train a GE model on new data to learn incessantly emerging multi-relational data while avoiding catastrophically forgetting old learned knowledge. Moreover, we propose a disentangle-b…
AL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learning
The Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州））；Northwestern Polytechnical University（西北工业大学）；South China University of Technology（华南理工大学）
Can LLMs Alleviate Catastrophic Forgetting in Graph Continual Learning? A Systematic Study
The Chinese University of Hong Kong（香港中文大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Science and Technology of China（中国科学技术大学）；University of Pittsburgh（匹兹堡大学）；Tongji University（同济大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
School of Computer Science and Engineering, Macau University of Science and Technology（澳门科技大学计算机科学与工程学院）；Department of Computer Science, Hong Kong Baptist University（香港浸会大学计算机科学系）
CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs
Technology, Innovation, Entrepreneurship Department（技术、创新与创业部门）；King Abdullah University of Science and Technology（卡塔尔科技大学）；School of Computing and Information Systems（计算与信息系统学院）；The University of Melbourne（墨尔本大学）；College of Computer Science and Technology（计算机科学与技术学院）；Jilin University（吉林大学）；School of Computing Technologies（计算技术学院）；RMIT University（皇家墨尔本理工大学）；College of Computing and Data Science（计算与数据科学学院）；Nanyang Technological University（南洋理工大学）
Department of Mathematics, University of Padua（帕多瓦大学数学系）；Faculty of Informatics, Università della Svizzera Italiana（瑞士意大利大学信息学院）；Department of Electronics, Information and Bioengineering, Politecnico di Milano（米兰理工学院电子、信息与生物工程系）
Despite significant advances, continual learning models still suffer from catastrophic forgetting when exposed to incrementally available data from non-stationary distributions. Rehearsal approaches alleviate the problem by maintaining and replaying a small episodic memory of previous samples, often implemented as an array of independent memory slots. In this work, we propose to augment such an array with a learnable random graph that captures pairwise similarities between its samples, and use it not only to learn new tasks but also to guard against forgetting. Empirical results on several ben…
RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间安全学院）；University of Science and Technology of China（中国科学技术大学）；Department of Computer Science, University of Illinois Chicago（伊利诺伊大学芝加哥分校计算机科学系）
G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs
School of Computer Science and Engineering, Beihang University（北航计算机科学与工程学院）；Department of Statistics, Columbia University（哥伦比亚大学统计系）；College of Computer Science, Beijing University of Technology（北京理工大学计算机学院）
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；ZJU-Ant Group Joint Research Center for Knowledge Graphs, Zhejiang University（浙江大学-蚂蚁集团知识图谱联合研究中心）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Monash University（莫纳什大学）；Hangzhou Dianzi University（杭州电子科技大学）；Zhejiang Lab（之江实验室）
University of Central Florida（中佛罗里达大学）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mobility is a sequential decision-making process dependent on the users' dynamic interests. With accurate user profiles, the predictive model can perfectly reproduce users' mobility trajectories. In the reverse direction, once the predictive model can imitate users' mobility patterns, the learned user profiles are also optimal. Such intuition motivates us to propose an imitation-based mobile user profiling framework by…
Dynamic Sub-graph Distillation for Robust Semi-supervised Continual Learning
Tianjin University（天津大学）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；Haihe Laboratory of Information Technology Application Innovation（海河信息技术应用创新实验室）
The University of Queensland（昆士兰大学）；Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）；University of Electronic Science and Technology of China（电子科技大学）
Meta-learning for few-shot learning allows a machine to leverage previously acquired knowledge as a prior, thus improving the performance on novel tasks with only small amounts of data. However, most mainstream models suffer from catastrophic forgetting and insufficient robustness issues, thereby failing to fully retain or exploit long-term knowledge while being prone to cause severe error accumulation. In this paper, we propose a novel Continual Meta-Learning approach with Bayesian Graph Neural Networks (CML-BGNN) that mathematically formulates meta-learning as continual learning of a sequenc…
Few-shot Continual Relation Extraction via Open Information Extraction
Hanoi University of Science and Technology（河内科技大学）；Oraichain Labs Inc.（Oraichain实验室公司）；VinAI Research（VinAI研究院）；Bytedance（字节跳动）；VNU University of Engineering and Technology（越南国家大学工程技术大学）；University of Oregon（俄勒冈大学）
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
University of Science and Technology Beijing（北京科技大学）；Monash University（莫纳什大学）；Shenzhen University（深圳大学）；Hainan University（海南大学）；Florida Atlantic University（佛罗里达大西洋大学）；Griffith University（格里菲斯大学）
Most of todays work on knowledge graph completion is concerned with sub-symbolic approaches that focus on the concept of embedding a given graph in a low dimensional vector space. Against this trend, we propose an approach called AnyBURL that is rooted in the symbolic space. Its core algorithm is based on sampling paths, which are generalized into Horn rules. Previously published results show that the prediction quality of AnyBURL is on the same level as current state of the art with the additional benefit of offering an explanation for the predicted fact. In this paper, we are concerned with …
University of Central Florida（中佛罗里达大学）；University of Macau（澳门大学）；Rutgers University（罗格斯大学）
Mobile user profiling refers to the efforts of extracting users' characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user's profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, …
Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer
School of Computer Engineering and Science, Shanghai University（上海大学计算机工程与科学学院）；School of Computer Science and Technology, Donghua University（东华大学计算机科学与技术学院）；School of Computer and Information Engineering, Shanghai Polytechnic University（上海第二工业大学计算机与信息工程学院）
We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline and then generalizes to previously unseen graphs without further training. This allows our approach to produce high-quality execution decisions on real-world TensorFlow graphs in seconds instead of hours. We consider two optimization tasks for computation graphs: minimizing running…
Beihang University（北京航空航天大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；University of Leeds（利兹大学）
Graph Neural Networks (GNNs) have been widely used for the representation learning of various structured graph data. While promising, most existing GNNs oversimplified the complexity and diversity of the edges in the graph, and thus inefficient to cope with ubiquitous heterogeneous graphs, which are typically in the form of multi-relational graph representations. In this paper, we propose RioGNN, a novel Reinforced, recursive and flexible neighborhood selection guided multi-relational Graph Neural Network architecture, to navigate complexity of neural network structures whilst maintaining rela…
Revisiting Catastrophic Forgetting in Continual Knowledge Graph Embedding
Data-intensive Technologies and Knowledge Systems (DTAK), Department of Service and Information System Engineering, Universitat Politècnica de Catalunya（数据密集型技术与知识系统（DTAK），服务与信息系统工程系，巴塞罗那理工大学）；Intelligent Data Science and Artificial Intelligence Research Center (IDEAI), Department of Computer Science, Universitat Politècnica de Catalunya（智能数据科学与人工智能研究中心（IDEAI），计算机科学系，巴塞罗那理工大学）
