Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Shi, Jieming"'
Publikováno v:
The VLDB Journal (2024) 1-31
Attributed networks containing entity-specific information in node attributes are ubiquitous in modeling social networks, e-commerce, bioinformatics, etc. Their inherent network topology ranges from simple graphs to hypergraphs with high-order intera
Externí odkaz:
http://arxiv.org/abs/2408.05459
Attributed bipartite graphs (ABGs) are an expressive data model for describing the interactions between two sets of heterogeneous nodes that are associated with rich attributes, such as customer-product purchase networks and author-paper authorship g
Externí odkaz:
http://arxiv.org/abs/2405.11922
Heterogeneous graphs are ubiquitous to model complex data. There are urgent needs on powerful heterogeneous graph neural networks to effectively support important applications. We identify a potential semantic mixing issue in existing message passing
Externí odkaz:
http://arxiv.org/abs/2405.01927
Autor:
Yang, Renchi, Shi, Jieming
A bipartite graph contains inter-set edges between two disjoint vertex sets, and is widely used to model real-world data, such as user-item purchase records, author-article publications, and biological interactions between drugs and proteins. k-Bipar
Externí odkaz:
http://arxiv.org/abs/2312.16926
Autor:
Ding, Zhihao, Shi, Jieming, Shen, Shiqi, Shang, Xuequn, Cao, Jiannong, Wang, Zhipeng, Gong, Zhi
Graph-level representation learning is important in a wide range of applications. Existing graph-level models are generally built on i.i.d. assumption for both training and testing graphs. However, in an open world, models can encounter out-of-distri
Externí odkaz:
http://arxiv.org/abs/2310.10237
Cryptocurrencies are rapidly expanding and becoming vital in digital financial markets. However, the rise in cryptocurrency-related illicit activities has led to significant losses for users. To protect the security of these platforms, it is critical
Externí odkaz:
http://arxiv.org/abs/2309.02460
The rise of the click farm business using Multi-purpose Messaging Mobile Apps (MMMAs) tempts cybercriminals to perpetrate crowdsourcing frauds that cause financial losses to click farm workers. In this paper, we propose a novel contrastive multi-view
Externí odkaz:
http://arxiv.org/abs/2308.02793
Graph anomaly detection (GAD) has gained increasing attention in recent years due to its critical application in a wide range of domains, such as social networks, financial risk management, and traffic analysis. Existing GAD methods can be categorize
Externí odkaz:
http://arxiv.org/abs/2307.15244
Autor:
Zhang, Qianru, Wang, Zheng, Long, Cheng, Huang, Chao, Yiu, Siu-Ming, Liu, Yiding, Cong, Gao, Shi, Jieming
Detecting anomalous trajectories has become an important task in many location-based applications. While many approaches have been proposed for this task, they suffer from various issues including (1) incapability of detecting anomalous subtrajectori
Externí odkaz:
http://arxiv.org/abs/2211.08415
Given a graph G where each node is associated with a set of attributes, and a parameter k specifying the number of output clusters, k-attributed graph clustering (k-AGC) groups nodes in G into k disjoint clusters, such that nodes within the same clus
Externí odkaz:
http://arxiv.org/abs/2102.03826