Knowledge graph (KG)-enhanced recommender systems have been widely studied for alleviating data sparsity by incorporating rich relational semantics. However, existing KG-based models struggle to effectively model long-tail items due to the lack of reliable user-item supervision, which limits the exploitation of KG knowledge for recommendation. To address these challenges, we propose CHAKG, a cross-modal hyperedge alignment framework that compensates for missing supervision through efficient and selective interaction transfer. CHAKG projects both the KG and the user-item interaction graph into latent hyperedge spaces, enabling lightweight modeling of high-order semantic co-occurrence between interaction-rich items and long-tail items without expensive multi-hop propagation. Building on this representation, CHAKG introduces a consistency-guided interaction transfer mechanism, which selectively transfers reliable signals from interaction-rich items to long-tail items via a learnable denoising process. Furthermore, a cross-modal contrastive alignment objective enforces semantic consistency between knowledge and interaction views, improving robustness and generalization under sparse supervision. Extensive experiments on four benchmark datasets demonstrate that CHAKG achieves state-of-the-art accuracy and efficiency, particularly under cold-start and long-tail recommendation scenarios.

Graph is the universal language for modeling data across various domains. In recent years, graph representation learning has achieved outstanding performance in a series of computational tasks on graphs, such as graph node classification and community detection, etc. However, a commonly hidden assumption underlying these tasks is that label information corresponds to nodes in the training set on a one-to-one basis. In real-world scenarios, node label information may be uncertain and concealed within higher-order graph structural labels. In this paper, we define a set of arbitrary nodes on the graph as a bag and assume that label information corresponds to bags rather than individual nodes. Furthermore, bag labels are generated based on node-level labels through some hidden mechanism, thus inexactly encompassing node label information1. Therefore, we propose for the first time a novel and widely applicable task: learning the latent representation of bags on the graph and predicting their labels. For this task, we propose a hierarchical model that is highly interpretable and scalable, incorporating information propagation among nodes, information aggregation from nodes to bags and inter-bag relationship prediction. Experiments on diverse standard datasets demonstrate that our proposed model shows higher classification accuracy compared to strong baselines. Our research introduces a new perspective to the field of graph representation learning.

Action-related KnowledGe (AKG) is important for facilitating deeper understanding of people’s life patterns, objectives and motivations. In this study, we present a novel framework for automatically predicting missing human biography records in Wikipedia by generating such knowledge. The generation method, which is based on a neural network matrix factorization model, is capable of encoding action semantics from diverse perspectives and discovering latent inter-action relations. By correctly predicting missing information and correcting errors, our work can effectively improve the quality of data about the behavioral records of historical figures in the knowledge base (e.g., biographies in Wikipedia), thus contributing to the understanding and study of human actions by the general public on the one hand, and can be considered as a new paradigm for managing action-related knowledge in digital libraries on the other. Extensive experiments demonstrate that the AKG we generate can capture well missing or “forgotten” human biography related information in Wikipedia.

In graph node classification tasks, traditional graph neural network (GNN) models assume that different types of misclassification have equal loss and thus seek to maximize the posterior probability of sample nodes under labeled classes. However, the graph data in realistic scenarios tend to follow unbalanced long-tail class distributions, making it difficult for GNN to accurately represent the minority class samples because of the overfitting tendency to the majority class features. To address this problem, in this paper we propose a novel GNN model, named Dual Cost-sensitive Graph Convolutional Network (DCSGCN). The DCSGCN is a two-tower model containing two sub-networks that compute the posterior probability and the misclassification cost separately. It uses the cost as complementary information in classification to correct the posterior probability under the minimal risk perspective. Furthermore, we propose a series of new methods to compute node cost labels based on graph topological information and node class distribution. Extensive experiments lead to the observation that DCSGCN outperforms the state-of-the-art model on diverse real-world imbalanced graphs.

Comparative summarization is an effective strategy to discover important similarities and differences in collections of documents biased to users' interests. A natural method of this task is to find important and corresponding content. In this paper, we propose a novel research task of automatic query-based across-time summarization in news archives as well as we introduce an effective method to solve this task. The proposed model first learns an orthogonal transformation between temporally distant news collections. Then, it generates a set of corresponding sentence pairs based on a concise integer linear programming framework. We experimentally demonstrate the effectiveness of our method on the New York Times Annotated Corpus.

Abstract We propose a novel way of utilizing and accessing information stored in news archives as well as a new style of investigating the history. Our idea is to automatically generate similar entity pairs given two sets of entities, one from the past and one representing the present. This allows performing entity-oriented mapping between different times. We introduce an effective method to solve the aforementioned task based on a concise integer linear programming framework. In particular, our model first conducts typicality analysis to estimate entity representativeness. It next constructs orthogonal transformation between the two entity collections. The result is a set of typical across-time comparables. We demonstrate the effectiveness of our approach on the New York Times dataset through both qualitative and quantitative tests.

Abstract Knowledge of entity histories is often necessary for comprehensive understanding and characterization of entities. Yet, the analysis of an entity’s history is often most meaningful when carried out in comparison with the histories of other entities. In this paper, we describe a novel task ofhistory-based entity categorizationandcomparison. Based on a set of entity-related documents which are assumed as an input, we determine latent entity categories whose members share similar histories; hence, we are effectively grouping entities based on the correspondences in their historical developments. Next, we generate comparative timelines for each determined group allowing users to elucidate similarities and differences in the histories of entities. We evaluate our approach on several datasets of different entity types demonstrating its effectiveness against competitive baselines.