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Jiahui Jin

7 accepted papers

2026

CasUGC: Aligning User-Generated Comment Evolution with Cascade Dynamics for Popularity Prediction

IJCAI 2026

Accurately predicting the popularity of information cascades facilitates the development of social media network applications. During the cascade propagation process, user-generated comments continually evolve, thereby substantially affecting overall information popularity. However, existing methods

Cited by 0Scholar
2025

Exploring Multimodal Relation Extraction of Hierarchical Tabular Data with Multi-task Learning

ACL 2025long

Relation Extraction (RE) is a key task in table understanding, aiming to extract semantic relations between columns. However, complex tables with hierarchical headers are hard to obtain high-quality textual formats (e.g., Markdown) for input under practical scenarios like webpage screenshots and sca…

2025

GER-LLM: Efficient and Effective Geospatial Entity Resolution with Large Language Model

EMNLP 2025

Geospatial Entity Resolution (GER) plays a central role in integrating spatial data from diverse sources. However, existing methods are limited by their reliance on large amounts of training data and their inability to incorporate commonsense knowledge. While recent advances in Large Language Models

2025

Manta: Enhancing Mamba for Few-Shot Action Recognition of Long Sub-Sequence

AAAI 2025technical

In few-shot action recognition (FSAR), long sub-sequences of video naturally express entire actions more effectively. However, the high computational complexity of mainstream Transformer-based methods limits their application. Recent Mamba demonstrates efficiency in modeling long sequences, but dire…

2025

Riding the Wave: Multi-Scale Spatial-Temporal Graph Learning for Highway Traffic Flow Prediction Under Overload Scenarios

IJCAI 2025

Highway traffic flow prediction under overload scenarios (HIPO) is a critical problem in intelligent transportation systems, which aims to forecast future traffic patterns on highway segments during periods of exceptionally high demand. Despite its importance, this problem has rarely been explored i

2024

DiG-In-GNN: Discriminative Feature Guided GNN-Based Fraud Detector against Inconsistencies in Multi-Relation Fraud Graph

AAAI 2024technical

Fraud detection on multi-relation graphs aims to identify fraudsters in graphs. Graph Neural Network (GNN) models leverage graph structures to pass messages from neighbors to the target nodes, thereby enriching the representations of those target nodes. However, feature and structural inconsistency…