21 accepted papers
Dynamic graph neural networks (DyGNNs) are widely used to model evolving interactions, but may fail under data distribution shift. Due to limited and unreliable interventions and insufficient disentanglement, the existing dynamic graph domain generalization approaches lead to suboptimal results. We
Long combination vehicle (LCV) plays a vital role in modern urban transportation, enhancing traffic efficiency and alleviating congestion. However, the inherent unique characteristics of LCV, including articulation joints and extended lengths, pose challenges to its operational reliability, compromi
Graph neural networks (GNNs) have achieved great success in node classification tasks. However, most graph neural networks are incomplete. For example, the reference of each article is subjectively introduced by the author in the citation network, which leads to an incomplete citation network, espec…
Large language models (LLMs) excel in natural language tasks, with Chain-of-Thought (CoT) prompting enhancing reasoning through step-by-step decomposition. However, CoT struggles in knowledge-intensive tasks with multiple entities and implicit multi-hop relations, failing to connect entities systema
Graph anomaly detection (GAD), which aims to identify patterns that deviate significantly from normal nodes in attributed networks, is widely used in financial fraud, cybersecurity, and bioinformatics. The paradigms of jointly optimizing contrastive learning and reconstruction learning have shown si
Anomaly detection on attributed graphs has applications in various domains such as finance and email spam detection, thus gaining substantial attention. Distributed scenarios can also involve issues related to anomaly detection in attribute graphs, such as in medical scenarios. However, most of the…
Subgraph representation learning has attracted growing interest due to its wide applications in various domains. However, existing methods primarily focus on local neighborhood structures while overlooking the significant impact of global structural information, in particular the influence of multi-…
Spatial-temporal forecasting (STF) plays a pivotal role in urban planning and computing. Spatial-Temporal Graph Neural Networks (STGNNs) excel at modeling spatial-temporal dynamics, thus being robust against noise perturbations. However, they often suffer from relatively poor computational efficienc…
Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute-object compositions by leveraging knowledge from seen compositions. Existing methods typically align textual prototypes with visual features using Vision-Language Models (VLMs), but they face two key limitations: (1) modality…
The rapid advancement of Multimodal Large Language Models (MLLMs) has led to remarkable performances across various domains. However, this progress is accompanied by a substantial surge in the resource consumption of these models. We address this pressing issue by introducing a new approach, Token R…
Graph Neural Networks (GNNs) excel in node classification tasks but often assume homophily, where connected nodes share similar labels. This assumption does not hold in many real-world heterophilic graphs. Existing models for heterophilic graphs primarily rely on pairwise relationships, overlooking
With the rapid advancement of large language models (LLMs), discrete speech representations have become crucial for integrating speech into LLMs. Existing methods for speech representation discretization rely on a predefined codebook size and Euclidean distance-based quantization. However, 1) the si…
Recent advancements in end-to-end text-to-speech models have made significant progress. However, these approaches based on high-resource languages, are inapplicable for low-resource languages, and existing low-resource speech synthesis methods are typically specific to single languages. Consequently…
Anomaly subgraph detection is a crucial task in various real-world applications, including identifying high-risk areas, detecting river pollution, and monitoring disease outbreaks. Early traditional graph-based methods can obtain high-precision detection results in scenes with small-scale graphs and…
In Community Question Answering (CQA) websites, most current expert finding methods often model expert embeddings from textual features and optimize them with expert-question first-order interactions, i.e., this expert has answered this question. In this paper, we try to address the limitation of cu…
Articulation angle plays a significant role in determining the motion of a complex dynamic system such as a multi-articulated vehicle. By engineering practice, articulation angles are measured using mechanical angle sensors that are delicate to physical damage. To overcome this problem, this study p…
Expert finding could help route questions to potential suitable users to answer in Community Question Answering (CQA) platforms. Hence it is essential to learn accurate representations of experts and questions according to the question text articles. Recently the pre-training and fine-tuning paradig…
Anomaly detection in the video is an important research area and a challenging task in real applications. Due to the unavailability of large-scale annotated anomaly events, most existing video anomaly detection (VAD) methods focus on learning the distribution of normal samples to detect the substant…
Personalized review summarization in recommender systems is a challenging task of generating condensed summaries for product reviews while preserving the salient content of reviews. Recently, Pretrained Language Models (PLMs) have become a new paradigm in text generation for the strong ability of na…
Object localization aims to generate a tight bounding box for the target object, which is a challenging problem that has been deeply studied in recent years. Since collecting bounding-box labels is time-consuming and laborious, many researchers focus on weakly supervised object localization (WSOL).…
Role-based network embedding methods aim to preserve node-centric connectivity patterns, which are expressions of node roles, into low-dimensional vectors. However, almost all the existing methods are designed for capturing a relaxation of automorphic equivalence or regular equivalence. They may be…