Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Ant Group（蚂蚁集团）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；School of Computer Science, Fudan University（复旦大学计算机学院）；School of Science, Computing and Engineering Technologies, Swinburne University of Technology（斯威本科技大学科学、计算与工程技术学院）
Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named LFH that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with eleven state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (average 12.5% in node classification and 13.3% in link prediction) compared with recent state-of-the-art methods.
Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits
University of Southern California（南加州大学）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Texas Tech University（德克萨斯理工大学）；Southern Methodist University（南卫理公会大学）
Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；School of Computer Science and Technology, Donghua University（东华大学计算机科学与技术学院）
Temporally Detailed Hypergraph Neural ODEs for Disease Progression Modeling
Department of Computer and Information Science and Engineering, University of Florida（佛罗里达大学计算机与信息科学与工程系）；Regenstrief Institute（Regenstrief研究所）；Department of Biostatistics and Health Data Science, Indiana University（印第安纳大学生物统计学与健康数据科学系）；Department of Pharmacy Practice, Purdue University（普渡大学药学实践系）
School of Cyberspace, Hangzhou Dianzi University（杭州电子科技大学信息学院）；Data Security Governance Zhejiang Engineering Research Center（浙江数据安全治理工程研究中心）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学院）
South China University of Technology（华南理工大学）；Communication and Computer Network Laboratory of Guangdong（广东通信与计算机网络实验室）；Peng Cheng Laboratory（鹏城实验室）；JD Finance America Corporation（京东金融美国公司）；University of Hong Kong（香港大学）
Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e.g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes. To tackle these challenges, we propose Spatial-Temporal Sequential Hypergraph Network (ST-SHN) to collectively encode complex crime spatial-temporal patterns as well as the underlying category-wi…
The University of Queensland（昆士兰大学）；University of Electronic Science and Technology of China（电子科技大学）
Modeling heterogeneity by extraction and exploitation of high-order information from heterogeneous information networks (HINs) has been attracting immense research attention in recent times. Such heterogeneous network embedding (HNE) methods effectively harness the heterogeneity of small-scale HINs. However, in the real world, the size of HINs grow exponentially with the continuous introduction of new nodes and different types of links, making it a billion-scale network. Learning node embeddings on such HINs creates a performance bottleneck for existing HNE methods that are commonly centralize…
Structural Controllability of Large-Scale Hypergraphs
Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard（MIT和哈佛大学布罗德研究所的埃里克和wendy Schmidt中心）；School of Data Science and Society, University of North Carolina at Chapel Hill（夏洛特希尔大学数据科学与社会学院）；Department of Biostatistics, University of North Carolina at Chapel Hill（夏洛特希尔大学生物统计学系）
