Predicting information diffusion is a fundamental task in online social networks (OSNs).Recent studies mainly focus on the popularity prediction of specific content but ignore the correlation between multiple pieces of information.The topic is often used to correlate such information and can correspond to multi-source information.The popularity of a topic relies not only on information diffusion time but also on users' followership.Current solutions concentrate on hard time partition, lacking versatility.Meanwhile, the hop-based sampling adopted in state-of-the-art (SOTA) methods encounters redundant user followership.Moreover, many SOTA methods are not designed with good modularity and lack evaluation for each functional module and enlightening discussion.This paper presents a novel extensible framework, coined as HIF, for effective popularity prediction in OSNs with four original contributions.First, HIF adopts a soft partition of users and time intervals to better learn users' behavioral preferences over time.Second, HIF utilizes weighted sampling to optimize the construction of heterogeneous graphs and reduce redundancy.Furthermore, HIF supports multi-task collaborative optimization to improve its learning capability.Finally, as an extensible framework, HIF provides generic module slots to combine different submodules (e.g., RNNs,

High frame-rate~(HFR) videos of action recognition improve fine-grained expression while reducing the spatio-temporal relation and motion information density. Thus, large amounts of video samples are continuously required for traditional data-driven training. However, samples are not always sufficient in real-world scenarios, promoting few-shot action recognition~(FSAR) research. We observe that most recent FSAR works build spatio-temporal relation of video samples via temporal alignment after spatial feature extraction, cutting apart spatial and temporal features within samples. They also capture motion information via narrow perspectives between adjacent frames without considering density, leading to insufficient motion information capturing. Therefore, we propose a novel plug-and-play architecture for FSAR called Spatio-tempOral frAme tuPle enhancer (SOAP) in this paper. The model we designed with such architecture refers to SOAP-Net. Temporal connections between different feature channels and spatio-temporal relation of features are considered instead of simple feature extraction. Comprehensive motion information is also captured, using frame tuples with multiple frames containing more motion information than adjacent frames. Combining frame tuples of diverse frame counts further provides a broader perspective. SOAP-Net achieves new state-of-the-art performance across well-known benchmarks such as SthSthV2, Kinetics, UCF101, and HMDB51. Extensive empirical evaluations underscore the competitiveness, pluggability, generalization, and robustness of SOAP. The code is released at https://github.com/wenbohuang1002/SOAP.

Information diffusion can be regarded as the process of multi-user collaboration to deliver information. How to predict the cascade size is a fundamental task and has many applications such as rumor detection, product marketing, etc. Recent works attempt to mine temporal and structural characteristics hidden in the information cascade based on deep learning models. As we know, the complicated interactions between nodes are critical for cascade size prediction, and these interactions are usually hidden in the multiple types of cascade relationships. However, the cascade relationship diversity has not been comprehensively exploited by current studies. In this paper, we propose a novel model named CTformer, which leverages the global receptive field of Transformer to make accurate prediction. Specifically, CTformer takes advantage of both global position encoding and bias matrices to explore cascade relationship diversity. Extensive evaluation results on multiple real-world datasets show that CTformer achieves significant performance gains over the state-of-the-art methods.

Popularity prediction is to predict the number of social network users involved in information diffusion. Recently, deep learning methods for popularity prediction advance traditional approaches that rely on hand-crafted features. However, existing approaches ignore the multi-source cascade that consists of multiple sub-cascades with different content but under the same topic. Different from single-source cascade, more cascading information can be observed from multi-source cascade and they are potentially correlated. How to correlate the diverse information and take advantage of them from both temporal and spatial aspects is critical for prediction. To this end, we propose a novel framework, called HEterogeneous Recurrent Integrated Graph Convolutional Neural Network (HERI-GCN). Specifically, we construct a heterogeneous cascade graph to model the multi-source cascade where time intervals are treated as heterogeneous time nodes. Besides, we propose a heterogeneous GCN to learn rich features from the multi-source cascade. RNN is organically integrated into the heterogeneous GCN to overcome the limited learning ability toward temporal and spatial data. We evaluate HERI-GCN through comparative experiments on three datasets. The experimental evaluation shows that HERI-GCN outperforms the state-of-the-art baseline methods.