Link prediction for attributed graphs has garnered significant attention due to its ability to enhance predictive performance by leveraging multi-modal node attributes. However, real-world challenges such as privacy concerns, content restrictions, and attribute constraints often result in nodes facing varying degrees of missing modalities in their attributes, significantly limiting the effectiveness of existing approaches. Building on this fact, we propose a model for linkPRediction in attrIbuted networkSwith uncertainModalities (PRISM), which learns the shared representations across various scenarios of missing modalities through dual-level adversarial training.PRISMcomprises four modules,i.e.,a GCN extractor, an adversarial extractor, an attentive fusion, and an adaptive aggregator. The GCN extractor leverages graph convolutional networks (GCN) to extract fundamental representations from the network topology. The adversarial extractor employs dual-level adversarial training to acquire the shared representations across various multi-modal scenarios at the node-level and link-level, respectively. The attentive fusion applies the multi-head attention mechanism to integrate the shared representations and the fundamental representations. The adaptive aggregator comprehensively considers both node-level and link-level representations to predict the existence of links. Experimental evaluation using real-world datasets demonstrates thatPRISMsignificantly outperforms existing state-of-the-art link prediction methods for multi-modal attributed graphs under missing modalities by improving the Recall@50 metric (R@50) by up to 38.79%.

Understanding structural regularities across layers in multi-layer networks is essential for uncovering their underlying generative mechanisms. While link prediction has been widely explored in multi-layer networks, it is typically treated as an isolated technical problem, often missing its broader implications for network structure and the mechanisms driving edge formation. In this article, we investigate the extent to which network layers exhibit shared generative regularities. By examining the alignment of latent representations across layers, we assess the similarity of their underlying mechanisms and leverage this alignment to improve predictive performance. To facilitate this, we introduce a new metric, C ross- L ayer G enerative C onsistency ( CLGC ), which quantitatively captures the degree of structural and generative alignment between network layers. CLGC is grounded in the shared-latent space framework, positing that layers generated by similar mechanisms will produce compatible latent representations. To realize this approach, we present SupportNet – Support prediction and consistency analysis in multi-layer Net works–a GCN-based model augmented with adversarial training to effectively learn robust shared-latent space representations. These representations support both accurate link prediction and interpretable evaluation of cross-layer generative consistency. Experiments on real-world multi-layer networks demonstrate that SupportNet delivers strong link prediction results improving AUC by 17.47%, AP by 40.41% and AUPR by 39.59% on the Kapferer dataset, while CLGC reveals significant patterns of structural and generative alignment among layers.

Drug-drug interactions (DDIs) can critically affect treatment safety and efficacy, especially when multiple drugs are prescribed concurrently. Such interactions may alter pharmacological activity and complicate therapeutic outcomes. Although graph-based learning methods have advanced DDI prediction, most rely solely on drug-drug networks, overlooking valuable information from auxiliary drug-centric networks. To overcome this limitation, it is crucial to adopt more hierarchical strategies that incorporate both drug-drug and auxiliary drug-centric networks to capture nuanced drug representations. In this research, we construct a hierarchical network to incorporate both drug-drug and auxiliary networks as distinct layers and propose a drug-centric hierarchical augmentation method (DCHA) for DDI prediction. DCHA encompasses three core components: a hierarchical learner, a layer discriminator, and a DDI predictor. The hierarchical learner employs a fusion gate to compute augmented drug representations by integrating core drug representations from the drug-drug network and auxiliary representations from other auxiliary drug-centric networks. The layer discriminator helps the hierarchical learner in capturing auxiliary drug representations. With the help of the hierarchical learner and layer discriminator, the DDI predictor finally augments the performance of DDI prediction. Extensive experimentation demonstrates that DCHA outperforms existing state-of-the-art methods in DDI prediction.

Large Language Models (LLMs) have demonstrated significant capabilities across numerous application domains. A key challenge is to keep these models updated with latest available information, which limits the true potential of these models for the end-applications. Although, there have been numerous attempts for LLMs’ Knowledge Editing (KE), i.e., to update and/or edit the LLMs’ prior knowledge and in turn test it via Multi-hop Question Answering (MQA), yet these studies are primarily focused on English language. In this paper, we extend MQA-Ke for Arabic language. For this, we propose: Multi-hop Questioning Answering under Knowledge editing for arabic language (MQA-Keal). MQA-Keal stores knowledge edits as structured knowledge units in the external memory. In order to solve multi-hop question, it first uses task-decomposition to decompose the question into smaller sub-problems. Later, for each sub-problem it iteratively queries the external memory and/or target LLM in order to generate the final response. In addition, we also contribute MQuAKE-ar (Arabic translation of English benchmark MQuAKE), as well as a new benchmark MQA-Aeval for rigorous performance evaluation of MQA under Ke for Arabic language. Experimentation evaluation reveals MQA-Keal outperforms the baseline models by a significant margin. We release the codes for MQA-Keal at https://github.com/asif6827/MQA-Keal.

In dynamic graphs, detecting anomalous nodes faces challenges due to sample imbalance, stemming from the scarcity of anomalous samples and feature representation bias. Existing methods often use unsupervised or semi-supervised learning to extract anomalous samples from unlabeled data, but struggle to obtain enough anomalous instances due to their low occurrence. Moreover, GNN-based approaches often prioritize normal samples, neglecting rare anomalies. To address these issues, we propose the Anomaly Balance Network (ABNet), designed to alleviate sample imbalance and enhance anomaly detection. ABNet includes three key components: a feature extractor that compares node features across time points to avoid bias, an anomaly augmenter that amplifies anomaly details and generates diverse anomalous samples, and an anomaly detector using meta-learning to adapt to graph evolution. Experimental results show that ABNet outperforms existing methods on three real-world datasets, effectively addressing sample imbalance.

Link prediction in multi-layer networks is a longstanding issue that predicts missing links based on the observed structures across all layers. Existing link prediction methods in multi-layer network typically merge the multi-layer network into a single-layer network and/or perform explicit calculations using intra-layer and inter-layer similarity metrics. However, these approaches often overlook the role of coupling in multi-layer networks, specifically the shared information and latent relationships between layers, which in turn limits prediction performance. This calls the need for methods that can extract representations in a shared-latent space to enhance inter-layer information sharing and prediction performance. In this paper, we propose a novel end-to-end framework namely: Link prediction Utilizing Shared-laTent spacE Representation (LUSTER) in multi-layer networks. LUSTER consists of four key modules: the representation extractor, the latent space learner, the complementary enhancer, and the link predictor. The representation extractor focuses on learning the intra-layer representations of each layer, capturing the data characteristics within the layer. The latent space learner extracts representations from the shared-latent space across different network layers through adversarial training. The complementary enhancer combines the intra-layer representations and the shared-latent space representations through orthogonal fusion, providing comprehensive information. Finally, the link predictor uses the enhanced representations to predict missing links. Extensive experimental analyses demonstrate that LUSTER outperforms state-of-the-art methods for link prediction in multi-layer networks, improving the AUC metric by up to 15.87%.

This paper studies how to determine temporal orders on attribute values in a set of tuples that pertain to the same entity, in the absence of complete timestamps. We propose a creator-critic framework to learn and deduce temporal orders by combining deep learning and rule-based deduction, referred to as GATE (Get the lATEst). The creator of GATE trains a ranking model via deep learning, to learn temporal orders and rank attribute values based on correlations among the attributes. The critic then validates the temporal orders learned and deduces more ranked pairs by chasing the data with currency constraints; it also provides augmented training data as feedback for the creator to improve the ranking in the next round. The process proceeds until the temporal order obtained becomes stable. Using real-life and synthetic datasets, we show that GATE is able to determine temporal orders withF-measure above 80%, improving deep learning by 7.8% and rule-based methods by 34.4%.

Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FG-NET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention's sentence-specific context. This is inadequate for highly overlapping and/or noisy type labels as it hinders information passing across sentence boundaries. For this, we propose an edge-weighted attentive graph convolution network that refines the noisy mention representations by attending over corpus-level contextual clues prior to the end classification. Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10.2% and 8.3% for macro-f1 and micro-f1 respectively.

Entity resolution (ER) aims to identify entity records that refer to the same real-world entity, which is a critical problem in data cleaning and integration. Most of the existing models are attribute-centric, that is, matching entity pairs by comparing similarities of pre-aligned attributes, which require the schemas of records to be identical and are too coarse-grained to capture subtle key information within a single attribute. In this paper, we propose a novel graph-based ER model GraphER. Our model is token-centric: the final matching results are generated by directly aggregating token-level comparison features, in which both the semantic and structural information has been softly embedded into token embeddings by training an Entity Record Graph Convolutional Network (ER-GCN). To the best of our knowledge, our work is the first effort to do token-centric entity resolution with the help of GCN in entity resolution task. Extensive experiments on two real-world datasets demonstrate that our model stably outperforms state-of-the-art models.

Distinguishing antonyms from synonyms is a key challenge for many NLP applications focused on the lexical-semantic relation extraction. Existing solutions relying on large-scale corpora yield low performance because of huge contextual overlap of antonym and synonym pairs. We propose a novel approach entirely based on pre-trained embeddings. We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper. Later, a classifier is trained based on features constructed from the distilled sub-spaces along with some word level features to distinguish antonyms from synonyms. Experimental results show that the proposed model outperforms existing research on antonym synonym distinction in both speed and performance.