Early detection of fake news is crucial to mitigate its negative impact. Current research in fake news detection often utilizes the difference between real and fake news regarding the support degree from reliable sources. However, it has overlooked their different semantic outlier degrees among unreliable source information during the same period. Since fake news often serves idea propaganda, unreliable sources usually publish a lot of information with the same propaganda idea during the same period, making it less likely to be a semantic outlier. To leverage this difference, we propose the Reliable-Unreliable Source Reference (RUSR) Fake News Early Detection Method. RUSR introduces the publication background for detected news, which consists of related news with common main objects of description and slightly earlier publication from both reliable and unreliable sources. Furthermore, we develop a strongly preference-driven support degree evaluation model and a two-hop semantic outlier degree evaluation model, which respectively mitigate the interference of news with weak validation effectiveness and the tightness degree of semantic cluster. The designed redistribution module and expanding range relative time encoding are adopted by both models, respectively optimizing early checkpoint of training and expressing the relevance of news implied by their release time gap. Finally, we present a multi-model mutual benefit and collaboration framework that enables the multi-model mutual benefit of generalization in training and multi-perspective prediction of news authenticity in inference. Experiments on our newly constructed dataset demonstrate the superiority of RUSR.

Fully inductive knowledge graph completion (KGC) aims to predict triplets involving both unseen entities and relations. Recent several approaches transform paths between entities into descriptions and modeling semantic correlations between paths using pre-trained language models (PLMs), have emerged as a promising solution for fully inductive reasoning . However, these methods often adopt a simplistic concatenation strategy for path-to-sentence transformation, which impedes PLMs’ ability to capture subtle nuances in context, resulting in sub-optimal path context embeddings. Furthermore, they ignore the high-order semantics underlying the complete context, which can provide richer information for inductive reasoning . To address these issues, we propose a Multi-Granularity Contextual Semantic (MGCS) modeling framework, utilizing a Path Modeling Network (PMN) and a Subgraph Modeling Network (SMN) to extract two granularity levels of contextual semantics from single paths and complete subgraphs, for fully inductive KGC. The PMN extracts paths between head and tail entities and employs reasoning patterns from similar cases to filter out unreliable paths. Then two innovative path conversion strategies are designed to significantly enhance the pre-trained language model’s understanding of specific path contexts. The SMN employs a neighbor interactive graph neural network to extract high-order semantics from the complete subgraph context with a concept-enhanced relation encoding, and optimizes it through a contrastive learning method. Finally, the confidence of the triples is evaluated from the perspective of global complete context by comparing the semantics between the subgraphs surrounding the target triplet and the subgraphs surrounding similar cases. Experimental results on benchmark datasets demonstrate the effectiveness of MGCS.

Traffic forecasting leverages multivariate time series analysis to predict traffic patterns. Real-world traffic data comprises two distinct types of latent time-series signals:diffuse signals, which refer to time-varying information propagated across the traffic network, andintrinsic signals, which capture unique, location-specific patterns. However, existing approaches often treat traffic signals solely as diffusion outcomes, overlooking the intrinsic characteristics that can significantly influence model performance. To address this issue, we propose the Position-invariant Graph Convolutional Recurrent Network (PGCRN), which decouples diffuse and intrinsic signals for improved traffic forecasting. Instead of relying on a predefined graph, PGCRN learns graph structures from spatio-temporal data through a learnable position-invariant node representation that forms an adaptive adjacency matrix. This is integrated into a Graph Convolutional Recurrent Network (GCRN) encoder–decoder to jointly capture spatial and temporal dependencies. Furthermore, we introduce a contrastive learning framework in which a node’s time-varying and position-invariant representations form positive pairs, while position-invariant representations from different nodes form negative pairs. The model is trained with a triplet loss. Experiments on four benchmark datasets show that PGCRN consistently outperforms strong baselines. Owing to its computational efficiency, PGCRN is also well suited for deployment on resource-constrained edge devices.

Question answering over knowledge graph (KGQA) aims at answering questions posed over the knowledge graph (KG). Multi-hop KGQA requires multi-hop reasoning on KG to achieve the correct answer. Unfortunately, KGs are usually incomplete with many missing links, which poses additional challenges to KGQA. KG embedding-based KGQA methods have recently been proposed as a way to overcome this limitation. However, existing KG embedding-based KGQA methods fail to take full advantage of semantic correlations between questions and paths. Furthermore, their inference process is not easily explainable. To address these challenges, we propose a novel path-aware multi-hop KGQA model (PA-KGQA), which can fully capture semantic correlations between the paths and the questions in a feature-interactive manner. Specifically, we introduce a case-enhanced path retriever to evaluate the importance of paths between topic entities and candidate answer entities, and then propose an interactive convolutional neural network (ICNN) to model the interactions between paths and questions for mining richer correlation features. Experiments show that PA-KGQA achieves state-of-the-art results on multiple benchmark datasets and is explainable.

Abstract Link prediction plays an important role in constructing knowledge graph. Recently, graph representation learning models yield state‐of‐the‐art results. However, existing models concentrate merely on triples or graph structures and mostly ignore textual descriptions, resulting in incomplete or partial information. In this paper, we propose a novel graph representation learning model to address this challenge, namely multi‐source heterogeneous information fusion with adaptive importance sampling. Our model leverages multiple sources, such triple, graph structure and textual description, and generate rich‐attribute embeddings for entities, encapsulating relations simultaneously. We also propose an adaptive importance sampling algorithm to boost aggregation of useful features from local neighbours. Additionally, we also boost node aggregation of useful features from local neighbours by adaptive importance sampling algorithm in our model. Experimental results on two benchmark datasets show that our proposed model significantly outperforms state‐of‐the‐art methods.

Knowledge Graph Completion (KGC) aims at completing the missing information in Knowledge Graphs (KGs) that provide a structured representation of knowledge. Most existing methods focus on completing existing entities and relationships that have not been discovered, while neglecting the importance of discovering new knowledge outsides KGs, especially the relationships, to the KGC. In this paper, we work further and try to detect relationship between two entities no matter they are inside or outside KGs. To complete this task, we propose a novel and unified model named Structured Attention Graph Neural Network (SAGNN) which can mine the new relationships outside KGs easily without relying on much external resource. By mining the latent association features from structural information such as in-degree, out-degree and co-occurrence frequency, the model enables specifying different weights to different nodes in a neighborhood even if the enter node lacks embedding representation, and generates the embedding with high-level semantic features eventually. Experiments on multiple datasets show that SAGNN performs well on the open-world relationship prediction task and even outperforms existing KGC models on the triple classification task in which entities outside KGs are involved.