The goal of community search in heterogeneous information networks (HINs) is to identify a set of closely related target nodes that includes a query target node. In practice, a size constraint is often imposed due to limited resources, which has been overlooked by most existing HIN community search works. In this paper, we introduce the size-bounded community search problem to HIN data. Specifically, we propose a refined (k, P)-truss model to measure community cohesiveness, aiming to identify the most cohesive community of size s that contains the query node. We prove that this problem is NP-hard. To solve this problem, we develop a novel B\&B framework that efficiently generates target node sets of size s. We then tailor novel bounding, branching, total ordering, and candidate reduction optimisations, which enable the framework to efficiently lead to an optimum result. We also design a heuristic algorithm leveraging structural properties of HINs to efficiently obtain a high-quality initial solution, which serves as a global lower bound to further enhance the above optimisations. Building upon these, we propose two exact algorithms that enumerate combinations of edges and nodes, respectively. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of the proposed methods.

Multi-modal Knowledge Graph Completion (MKGC) aims at leveraging multi-modal information to infer missing objective facts in incomplete multi-modal knowledge graphs, thereby significantly enhancing their expressive capabilities. The segmentation of semantic data, including image segmentation and word-level descriptions, often contain implicit relationships between entities that are frequently overlooked by existing methodologies, thus limiting the effectiveness of reasoning tasks. Therefore, we propose a novel completion inference method based on fine-grained semantic segmentation, which enhances reasoning capability by utilizing implicit relationships between entities. Primarily, we introduce the concept of Semantic Related Entity (SRE) and a novel SRE selection algorithm, which captures the semantic neighboring relationships of entities based on segmentation semantic similarity to fully exploit the semantic association information. Subsequently, we propose a Multi-modal Related Entity Fusion Transformer (M-REFT) model to effectively utilize SREs from semantic modalities and neighbors from structural modality for completion inference. The M-REFT employs a hierarchical Transformer architecture to encode the fusion modality representation between each entity and its SREs, and then decode the triplet representation with the neighbor information to identify missing entities in incomplete triplets. We conducted extensive comparative experiments with several state-of-the-art models on three datasets, demonstrating the significant performance advantages of M-REFT. A series of ablation experiments and case studies further validate the rationality and necessity of the SRE concept and the SRE selection algorithm.

Graph data has been widely applied due to its powerful expressive capabilities. The release of raw graph data without preprocessing may lead to privacy information leakage. Thus, generating privacy-protected graphs is necessary for data analysis. Current privacy protection methods in graphs focus on securing attributes like degree distribution, triangle counts, and node information, but they often overlook the need to protect user group relationships. Additionally, some privacy-preserving graph publishing methods introduce significant noise due to the chosen graph generation techniques and the points at which noise is added. This paper aims to propose an effective graph synthesis algorithm by using differential privacy named DPCAG (Differentially Private Community Affiliation Graph Generation Model) for protecting user group relationships. Firstly, it is observed that there are numerous small probabilities in the adjacency matrix D generated by the affiliation matrix F, directly utilizing it to construct graph G would result in the generation of a substantial number of redundant edges. Therefore, we introduce a generating threshold theta to filter out unnecessary edges. Secondly, to achieve a better balance between data availability and the level of privacy protection, two budget allocation schemes are designed based on the introduction of k-truss to describe the tightness of group relationships. Lastly, we demonstrate the proposed model satisfies differential privacy mathematically and the effectiveness of DPCAG is validated using four real graph datasets.

Abstract Hierarchical Text Classification (HTC) is a formidable task which involves classifying textual descriptions into a taxonomic hierarchy. Existing methods, however, have difficulty in adequately modeling the hierarchical label structures, because they tend to focus on employing graph embedding methods to encode the hierarchical structure while disregarding the fact that the HTC labels are rooted in a tree structure. This is significant because, unlike a graph, the tree structure inherently has a directive that ordains information flow from one node to another—a critical factor when applying graph embedding to the HTC task. But in the graph structure, message-passing is undirected, which will lead to the imbalance of message transmission between nodes when applied to HTC. To this end, we propose a unidirectional message-passing multi-label generation model for HTC, referred to as UMP-MG. Instead of viewing HTC as a classification problem as previous methods have done, this novel approach conceptualizes it as a sequence generation task, introducing prior hierarchical information during the decoding process. This further enables the blocking of information flow in one direction to ensure that the graph embedding method is better suited for the HTC task and thus resulted in the enhanced tree structure representation. Results obtained through experimentation on both the public WOS dataset and an E-commerce user intent classification dataset demonstrate that our proposed model can achieve superlative results.