Heterogeneous Graph Neural Networks (HGNNs) are widely used for deep learning on heterogeneous graphs. Typical end-to-end HGNNs require repetitive message passing during training, limiting efficiency for large-scale real-world graphs. Pre-computation-based HGNNs address this by performing message passing only once during preprocessing, collecting neighbor information into regular-shaped tensors, which enables efficient mini-batch training. Label-based pre-computation methods collect neighbors' label information but suffer from training label leakage, where a node's own label information propagates back to itself during multi-hop message passing—the echo effect. Existing mitigation strategies are memory-inefficient on large graphs or suffer from compatibility issues with advanced message passing methods. We propose Echoless Label-based Pre-computation (Echoless-LP), which eliminates training label leakage with Partition-Focused Echoless Propagation (PFEP). PFEP partitions target nodes and performs echoless propagation, where nodes in each partition collect label information only from neighbors in other partitions, avoiding echo while remaining memory-efficient and compatible with any message passing method. We also introduce an Asymmetric Partitioning Scheme (APS) and a PostAdjust mechanism to address information loss from partitioning and distributional shifts across partitions. Experiments on public datasets demonstrate that Echoless-LP achieves superior performance and maintains memory efficiency compared to baselines.

Micro-videos, characterized by their brief duration and rich multi-modal content, have become increasingly popular on digital platforms. Micro-Video Popularity Prediction (MVPP) is crucial for understanding user behavior, identifying emerging trends, and leveraging commercial opportunities. While existing MVPP methods predominantly focus on extracting features from the multi-modal content of micro-videos, they often overlook the valuable social interactions that occur within micro-video platforms. In this paper, we introduce a novel Social-Aware Multimodal Graph Learning (SAMGL) approach that integrates multi-modal content and social information to enhance MVPP. First, we propose Social-Aware Multimodal Precomputation (SAMP) to efficiently utilize complete graph information in micro-video popularity prediction, aggregating multi-modal signals to mitigate information loss. Second, we introduce Retrieval Augmentation Prediction (RAP), which retrieves similar micro-videos to enhance prediction by incorporating global content features beyond direct graph connections. To facilitate our approach, we construct the datasets by incorporating social interactions as graph information. Extensive experiments on the datasets demonstrate that our model achieves state-of-the-art performance, validating the effectiveness of integrating social information and the proposed local-global aggregation mechanisms.

Text-Attributed Heterogeneous Graphs (TAHGs), which combine text data with various graph relationship information linked to rich semantic entities, are ubiquitous in real-world scenarios. To extract information from TAHGs, a commonly used method is employing Pretrained Language Models (PLMs). However, existing methods are primarily designed for processing text and face challenges when dealing with graph information, leading to two main issues: incomplete context due to graph sampling and weak integration of text and graph information. In this article, we present a new approach named Metapath-Enhanced Language Model Pretraining (MLMP) on Text-Attributed Heterogeneous Graphs. The proposed model starts by gathering metapath information through pre-computed neighbor aggregation using a simple mean aggregator. Subsequently, this gathered metapath information, combined with textual data, is input into a GNN-nested PLM. Here, GNN components at each layer are nested alongside the transformer blocks of PLMs during the training process. We have also developed corresponding pretraining strategies for joint pretraining. The experimental results indicate that our model efficiently captures information within TAHGs. Across benchmark datasets, it consistently outperforms current state-of-the-art methods, demonstrating remarkable effectiveness in tasks such as link prediction and node classification. Our code is available at https://github.com/chensh911/MLMP .

Short-video platforms have become a central part of digital content, with users rapidly engaging in various trending topics. Predicting the peak popularity of short-video topics is critical for understanding content dynamics and user behavior. This paper introduces the task of Short-Video Topic Peak Prediction (SVTPP) and proposes both a new dataset and an innovative method. We present the TopicVid dataset, designed to capture the peak trends of short-video topics across multiple platforms. The TopicVid dataset includes 7,701 topics, 58,539 users, and 96,936 videos, with data on views, comments, and shares. This dataset is the first to provide rich semantic information for short-video topic peak prediction, including content details, titles, and user interactions. We propose the Topic Large Graph Model (TLGM), a two-stage framework that integrates heterogeneous graph data with large language models. The TLGM model effectively analyzes the relationships within short-video topics to predict their peak popularity. Experimental results show that our method outperforms existing approaches for predicting short-video topic peaks. Our dataset and code is available at https://github.com/chensh911/TLGM.