The proliferation of rumors on social networks poses a serious threat to cybersecurity, justice and public trust, increasing the urgent need for rumor detection. Existing detection methods typically treat all rumors as a single homogeneous category, neglecting the diverse semantic hierarchies within rumors. Rumors pervade various domains, each with its distinct characteristics. These methods tend to lag in expressiveness when confronted with real-world scenarios involving multiple semantic levels . Furthermore, the diversity of rumors also complicates the collection of datasets, and inevitably introduces noisy data, which hinders the correctness of the learned representations. To address these challenges, we propose a rumor detection framework with Hierarchical Prototype Contrastive Learning (HPCL). In this framework, we construct a set of dynamically updated hierarchical prototypes through contrastive learning to encourage capturing the hierarchical semantic structure within rumors. Additionally, we design a difficulty metric function based on the distance between instances and prototypes, and introduce curriculum learning to mitigate the adverse effects of noisy data. Experiments on four public datasets demonstrate that our approach achieves state-of-the-art performance. Our code is publicly released at https://github.com/Coder-HenryZa/HPCL .

Temporal relation prediction in incomplete temporal knowledge graphs (TKGs) is a popular temporal knowledge graph completion (TKGC) problem in both transductive and inductive settings. Traditional embedding-based TKGC models (TKGE) rely on structured connections and can only handle a fixed set of entities, i.e., the transductive setting. In the inductive setting where test TKGs contain emerging entities, the latest methods are based on symbolic rules or pre-trained language models (PLMs). However, they suffer from being inflexible and not time-specific, respectively. In this work, we extend the fully-inductive setting, where entities in the training and test sets are totally disjoint, into TKGs and take a further step towards a more flexible and time-sensitive temporal relation prediction approach SST-BERT,incorporating Structured Sentences with Time-enhanced BERT. Our model can obtain the entity history and implicitly learn rules in the semantic space by encoding structured sentences, solving the problem of inflexibility. We propose to use a time masking MLM task to pre-train BERT in a corpus rich in temporal tokens specially generated for TKGs, enhancing the time sensitivity of SST-BERT. To compute the probability of occurrence of a target quadruple, we aggregate all its structured sentences from both temporal and semantic perspectives into a score. Experiments on the transductive datasets and newly generated fully-inductive benchmarks show that SST-BERT successfully improves over state-of-the-art baselines.

In the last few years, the solution to Knowledge Graph (KG) completion via learning embeddings of entities and relations has attracted a surge of interest. Temporal KGs(TKGs) extend traditional Knowledge Graphs (KGs) by associating static triples with timestamps forming quadruples. Different from KGs and TKGs in the transductive setting, constantly emerging entities and relations in incomplete TKGs create demand to predict missing facts with unseen components, which is the extrapolation setting. Traditional temporal knowledge graph embedding (TKGE) methods are limited in the extrapolation setting since they are trained within a fixed set of components. In this paper, we propose a Meta-Learning based Temporal Knowledge Graph Extrapolation (MTKGE) model, which is trained on link prediction tasks sampled from the existing TKGs and tested in the emerging TKGs with unseen entities and relations. Specifically, we meta-train a GNN framework that captures relative position patterns and temporal sequence patterns between relations. The learned embeddings of patterns can be transferred to embed unseen components. Experimental results on two different TKG extrapolation datasets show that MTKGE consistently outperforms both the existing state-of-the-art models for knowledge graph extrapolation and specifically adapted KGE and TKGE baselines.

Stochastic Gradient Descent (SGD) series optimization methods play the vital role in training neural networks, attracting growing attention in science and engineering fields of the intelligent system. The choice of learning rates affects the convergence rate of SGD series optimization methods. Currently, learning rate adjustment strategies mainly face the following problems: (1) The traditional learning rate decay method mainly adopts manual manner during training iterations, the small learning rate produced from which causes slow convergence in training neural networks. (2) Adaptive method (e.g., Adam) has poor generalization performance. To alleviate the above issues, we propose a novel automatic learning rate decay strategy for SGD optimization methods in neural networks. On the basis of the observation that the convergence rate's upper bound enjoys minimization in a specific iteration concerning the current learning rate, we first present the expression of the current learning rate determined by historical learning rates. And merely one extra parameter is initialized to generate automatic decreasing learning rates during the training process. Our proposed approach is applied to SGD and Momentum SGD optimization algorithms, and concrete theoretical proof explains its convergence. Numerical simulations are conducted on the MNIST and Cifar-10 data sets with different neural networks. Experimental results show that our algorithm outperforms existing classical ones, achieving faster convergence rate, better stability, and generalization performance in neural network training. It also lays a foundation for large-scale parallel search of initial parameters in intelligent systems.