Hyperedge prediction plays a central role in hypergraph learning, enabling the inference of high-order relations among multiple entities. However, existing methods often rely on a simplistic flat set assumption, treating candidate hyperedges as unstructured collections of nodes and neglecting their potential internal compositionality. Furthermore, the severe scarcity of observed hyperedges poses a challenge for effective supervision. In this work, we propose S3Hyper, a Substructure-contextualized Self-Supervised framework for Hyperedge prediction, which jointly addresses these two challenges. Specifically, we design a substructure-contextualized hyperedge aggregator that models the internal hierarchy of candidate hyperedges by leveraging sub-hyperedge information. In parallel, we introduce an adaptive tri-directional contrastive learning module that incorporates node-level, hyperedge-level, and cross-level alignment objectives, supported by temperature-adaptive mechanisms. Experimental results on four public datasets demonstrate that S3Hyper consistently outperforms strong baselines, with ablation studies verifying the effectiveness of each component.

Understanding user profiling on social media poses significant challenges due to the intertwined complexities of network structures, user interactions, and the multi-dimensional nature of the data. To reduce visual clutter and offer complementary perspectives for engagingly exploring user profiling within these networks, we propose Social Media Island, an interactive 3D metaphoric visualization system. Our system uses 3D mountain metaphors to visualize user profiling, capturing user influence and activity, while tree metaphors visualize the information forwarding process. To support a flexible scope for users to explore in a more intriguing way, we design various interactions such as cutting the mountain to split out a subset with similarity to some extent for further exploration. By using these 3D visualizations with user interactions, Social Media Island facilitates immersion in the data, the fluid exploration of user influence, topic evolution, and the spread of information. The effectiveness of metaphors is evaluated by user studies, and that of the system is evaluated through two case studies.

Referring expression comprehension (REC) is a crucial task in understanding how a given text description identifies a target object within an image. Existing two-stage REC methods have demonstrated strong performance due to their rational framework design. However, during the encoding of object candidates in an image, most two-stage methods rely exclusively on features extracted from pre-trained detectors, often neglecting the contextual relationships between an object and its neighboring elements. This limitation hinders the full capture of contextual and relational information, reducing the discriminative power of object representations and negatively impacting subsequent processing. In this paper, we propose two novel plug-and-adapt modules: expression-guided label representation module (ELR) and cross-modal calibrated semantic module (CCS), designed to enhance two-stage REC methods. Specifically, the ELR module connects the noun phases of expression to the categorical labels of object candidates in the image, ensuring effective alignment between them. Guided by these connections, a CCS module is introduced to represent each object candidate by integrating its features with those of neighboring candidates from multiple perspectives. This preserves the intrinsic information of each candidate while incorporating relational cues from other objects, enabling more precise embeddings and effective downstream processing in two-stage REC methods. Extensive experiments on six datasets demonstrate the importance of incorporating prior statistical knowledge, and detailed analysis shows that the proposed modules strengthen the alignment between image and text. As a result, our method achieves competitive performance and is compatible with most two-stage methods in the REC task. The code is available on Github: https://github.com/freedom6927/ELR_CCS.git.