Temporal Knowledge Graphs (TKGs) serve as crucial tools for representing dynamic changes in the real world. Extrapolation reasoning within TKGs aims to predict entirely unknown future facts based on limited historical data, offering considerable practical value across various fields. However, existing methods generally focus on the recurrence and periodicity of historical facts, while overlooking the dynamic interactions associated with future facts. Moreover, these methods fail to capture historical evolutionary patterns, which grow increasingly complex as historical data accumulates. To this end, we propose TCPN, a novel Temporal Pyramidal Recurrent Network with contrastive learning for TKG extrapolation reasoning. Specifically, TCPN leverages a temporal pyramidal recurrent network to capture historical dependencies across multiple temporal scales, thereby refining temporal feature representations over extended time spans. Furthermore, TCPN seamlessly integrates contrastive learning to effectively align historical information with query semantics relevant to future facts. Lastly, we incorporate an adaptive time-aware mechanism, which uniformly models long-short term dependencies in time series with different granularities, explicitly fusing temporal feature information. Extensive experiments on four widely used TKG datasets show that TCPN significantly outperforms state-of-the-art methods across all metrics.

Extrapolation reasoning on Temporal Knowledge Graphs (TKGs) plays a pivotal role in various systems, including retrieval, recommendation, and Q&A. Traditional TKG reasoning methods tend to emphasize modeling the local and global features of facts, often overlooking the alignment with query semantics. Crucially, these methods are challenging in generating explicit reasoning paths. To address these gaps, we propose an innovative framework (LogiQ) for extrapolation reasoning on TKGs, steered by temporal logic rules and queries. Specifically, LogiQ incorporates temporal logic rules and implements a rule-guided reward mechanism, directing Reinforcement Learning (RL) agents toward actions more aligned with rules. Additionally, LogiQ merges temporal queries with neighbor aggregation, ensuring that candidate actions not only encapsulate neighboring factual data but also embody query semantics. This dual focus enables LogiQ to guide actions to find reasoning paths in limited steps strategically. Extensive experiments on four real-world TKG datasets demonstrate the superior performance of LogiQ across all metrics compared to existing state-of-the-art models.

Session-based recommendation (SBR) provides personalized recommendations by analyzing the interactions of anonymous session users. Recent approaches based on graph neural networks (GNNs) focus on pairwise relations to infer potential user preferences. However, real-world user interactions are often complex, involving high-order relations that GNNs may not fully capture. Hypergraphs can naturally model high-order relations, while having untapped potential in SBR. In this paper, we propose a hypergraph convolutional network (MoHyNet) based on hypergraph motifs to improve SBR. Specifically, we construct a hypergraph convolution to extract high-order relations among users, thereby reducing the impact of intra-session irrelevant information on recommendations. Additionally, we introduce hypergraph motifs to characterize users’ behavioral patterns, thus enhancing inter-session information mining. Besides, we incorporate contrastive learning to strengthen the representation of the current session. Extensive experiments on multiple real-world datasets demonstrate the superiority of our proposed model over state-of-the-art approaches.

Graph Edit Distance (GED) is a classical graph similarity metric. Since exact GED computation is NP-hard, existing GNN-based methods try to approximate GED in polynomial time. However, they still lack support for edge labels or the ability to generate an edit path. To address these limitations, we propose a hybrid method named CGEDN based on Graph Neural Networks and learnable node matching. Specifically, CGEDN starts with cross-graph feature aggregation layers, which generate node embeddings with fine-grained interaction features and support edge labels as well. Next, two node matching pipelines are applied to obtain a node matching confidence matrix and a cost matrix, where the confidence matrix is supervised by optimal node matchings corresponding to GED. Finally, we calculate the weighted sum of the cost matrix with a bias value to regress GED only, or search for the top-k most promising node matchings based on confidence matrix to approximate GED and recover an edit path simultaneously. The experimental results on real and synthetic graphs demonstrate that CGEDN significantly outperforms the best result of existing approximate methods.

Class imbalance with node quantity or node topology challenges graph node classification in real-world, such as fake user identification and fraud finance detection in social networks. Current studies mitigate the detrimental effects of class imbalance on graph neural networks through data augmentation and weight adjustment. However, these methods generate unreliable data and lack global generalizability, which results in poor classification performance for class-imbalanced nodes. To tackle the above issues, we propose a Graph Diffusion Enhanced Hypergraph method (GraphDEH), which introduces a hypergraph to capture high-order data correlations, strengthening semantic relational space of nodes. Specifically, we design a generator to constructs reliable hyperedges, which mitigate insufficient learning of minority classes caused by node quantity imbalance. Furthermore, we design an enhancer that employs graph diffusion to evaluate global influence among nodes, which increases homogeneous information in neighborhoods of minority-class nodes for node topology imbalance. GraphDEH outperforms multiple benchmark methods in class-imbalanced node classification across four benchmark datasets.

Tingxuan Chen, Jun Long, Liu Yang, Zidong Wang, Yongheng Wang, Xiongnan Jin. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Temporal Knowledge Graphs (TKGs) serve as indispensable tools for dynamic facts storage and reasoning. However, predicting future facts in TKGs presents a formidable challenge due to the unknowable nature of future facts. Existing temporal reasoning models depend on fact recurrence and periodicity, leading to information degradation over prolonged temporal evolution. In particular, the occurrence of one fact may influence the likelihood of another. To this end, we propose THCN, a novel Temporal Causal Convolutional Network based on Hawkes processes, designed for temporal reasoning under the extrapolation setting. Specifically, THCN harnesses a temporal causal convolutional network with dilated factors to capture historical dependencies among facts spanning diverse time intervals. Then, we construct a conditional intensity function based on Hawkes processes for fitting the likelihood of fact occurrence. Importantly, THCN pioneers a dual-level dynamic modeling mechanism, enabling the simultaneous capture of the collective features of nodes and the individual characteristics of facts. Extensive experiments on six real-world TKG datasets demonstrate our method significantly outperforms the state-of-the-art across all four evaluation metrics, indicating that THCN is more applicable for extrapolation reasoning in TKGs.

Surgical phase segmentation plays an important role in computer-assisted surgery systems, aiming to recognize what step or what action is operating in the video frame. Existing methods focus on improving the accuracy and precision of video segmentation, but ignore semantic consistency and temporal continuity of video frames in the intra-phase, which is necessary to apply in realistic computer-assisted equipment. Meanwhile, recent works almost extract long-term dependencies by Temporal Convolutional Network, but we heed high layers in TCN lose fine-grained information for detecting surgical steps and further affect phase segmentation task. To address these problems, we propose a Surgical Phase Segmentation Framework (SPHASE) which contains a multimodal feature fusion process and follows a multi-branch predictor. Moreover, we design a multimodal feature fusion mechanism when aggregate optical flow feature and I3D feature. The extensive experiments on AutoLaparo, Cholec80, and M2CAI2016 datasets demonstrate our method outperforms the state-of-the-art method by a large margin, especially in the JACC metric, which means SPHASE is more applicable in the surgical operating room.

In this paper, we consider the robust finite-horizon filter design problem for a class of discrete time varying systems with both stochastic nonlinearities and deterministic uncertainties. The description of the stochastic nonlinearities is quite general, which comprises the state-multiplicative noises and the random sequences whose powers depend on either the sector-bound nonlinear function of the state or the sign of a nonlinear function of the state. The norm bounded parameter uncertainties are allowed to enter both the system and the output matrices. We aim to design a robust filter that guarantees an optimized upper bound on the state estimation error variance, for all stochastic nonlinearities and admissible deterministic uncertainties. The existence conditions for the desired robust filters are first derived, and the filter parameters are then determined in terms of the solutions to two recursive Riccati-like difference equations. A numerical example is presented to show the applicability of the proposed method.