Event sequences are integral to domains such as e-commerce, social networks, and healthcare. Traditional point process models, like Poisson and Hawkes processes, are foundational but limited by rigid parametric assumptions, constraining their flexibility in complex real-world scenarios. Neural point processes offer a more adaptable alternative, but typically perform implicit sequence modeling, which does not fully exploit critical event interaction patterns and limits transparency. To address these challenges, we introduce the Explicit Interaction-aware Attention Network (EIAN), a novel model that enhances event modeling by explicitly capturing both intra-type and cross-type event interactions. Specifically, EIAN employs two key components: an intra-type temporal encoder that preserves the unique temporal dynamics within each event type, and a cross-type interaction decoder that highlights interactions across event types. Furthermore, two temporal encoding mechanisms are integrated into the interaction decoder to handle irregular inter-event intervals in diverse temporal scenarios. Extensive experiments show that EIAN consistently outperforms existing models in predictive performance and provides deeper insights into event interaction patterns, advancing both flexibility and interpretability. Our code is available at https://github.com/CGCL-codes/EIAN.git.

Heterogeneous graph neural networks(HGNNs) excel at understandingheterogeneous information networks(HINs) and have demonstrated state-of-the-art performance across numerous tasks. However, previous works tend to study small datasets, which deviate significantly from real-world scenarios. More specifically, their heterogeneous message passing results in substantial memory and time overheads, as it requires aggregating heterogeneous neighbor features multiple times. To address this, we propose anEfficient Heterogeneous Graph Neural Network(EHGNN) that leveragesheterogeneous personalized PageRank(HPPR) to preserve the influence between all nodes, then approximates message passing and selectively loads neighbor information for one aggregation, significantly reducing memory and time usage. In addition, we employ some lightweight techniques to ensure the performance of EHGNN. Evaluations on various HIN benchmarks in node classification and link prediction tasks unequivocally establish the superiority of EHGNN, surpassing the state-of-the-art by 11$\%$in terms of performance. In addition, EHGNN achieves a remarkable 400$\%$boost in training and inference speed while utilizing less memory. Notably, EHGNN can handle a 200-million-node, 1-billion-link HIN within 18 hours on a single machine, using only 170GB of memory, which is much lower than the previous minimum requirement of 600 GB. Our code is available athttps://github.com/CGCL-codes/EHGNN.

Heterogeneous Graph Neural Networks (HGNNs) play a vital role in advancing the field of graph representation learning by addressing the complexities arising from diverse data types and interconnected relationships in real-world scenarios. However, traditional HGNNs face challenges when applied to large-scale graphs due to the necessity of training or inferring on the entire graph. As the size of the heterogeneous graphs increases, the time and memory overhead required by these models escalates rapidly, even reaching unacceptable levels. To address this issue, in this paper, we present a novel framework named (SubInfer), which conducts training and inferring on subgraphs instead of the entire graphs, hence efficiently handling large-scale heterogeneous graphs. The proposed framework comprises three main steps: 1) partitioning the heterogeneous graph from multiple perspectives to preserve various semantic information, 2) completing the subgraphs to improve the convergence speed of subgraph training and the performance of subgraph inference, and 3) training and inferring the HGNN model on distributed clusters to further reduce the time overhead. The framework is applicable to the vast majority of HGNN models. Experiments on five benchmark datasets demonstrate that SubInfer effectively optimizes the training and inference phase, delivering comparable performance to traditional HGNN models while significantly reducing time and memory overhead.

Previous works for time series classification tend to assume that both the training and testing sets originate from the same distribution. This oversimplification deviates from the complexity of reality and makes it challenging to generalize methods to out-of-distribution (OOD) time series data. Currently, there are limited works focusing on time series OOD generalization, and they typically disentangle time series into domain-agnostic and domain-specific features and design tasks to intensify the distinction between the two. However, previous models purportedly yielding domain-agnostic features continue to harbor domain-specific information, thereby diminishing their adaptability to OOD data. To address this gap, we introduce a novel model called Invariant Time Series Representation (ITSR). ITSR achieves a learnable orthogonal decomposition of time series using two sets of orthogonal axes. In detail, ITSR projects time series onto these two sets of axes separately and obtains mutually orthogonal invariant features and relevant features. ITSR theoretically ensures low similarity between these two features and further incorporates various tasks to optimize them. Furthermore, we explore the benefits of preserving orthogonality between invariant and relevant features for OOD time series classification in theory. The results on four real-world datasets underscore the superiority of ITSR over state-of-the-art methods and demonstrate the critical role of maintaining orthogonality between invariant and relevant features. Our code is available at https://github.com/CGCL-codes/ITSR.