Hypergraphs, by extending traditional graphs with hyperedges, enable the modeling and prediction of complex higher-order interactions that go beyond simple pairwise interactions. Hyperedge prediction, an evolution of link prediction, aims to identify potential higher-order interactions—such as those in social media group chats—by recognizing and predicting hyperedges. Recently, hypergraph neural networks (HGNNs) have advanced hyperedge prediction by structuring higher-order interactions into a hypergraph, enabling effective capture of higher-order relations through information propagation across the hypergraph. However, existing methods primarily focus on developing complex HGNNs, underestimating the inherent unreliability of the underlying hypergraph due to incompleteness and noise, leading to suboptimal and fragile performance. In this article, we propose Multi-HyperLinker, a novel multi-view hypergraph learning framework that leverages the consistency and invariance across multiple views to capture reliable higher-order interaction patterns from historical observational data for robust hyperedge prediction. Specifically, to facilitate effective information propagation on incomplete hypergraphs, Multi-HyperLinker first synthesizes a tightly structured hypergraph and designs a consistency-guided dual-view learning strategy. To capture reliable higher-order interaction patterns on noisy hypergraphs, Multi-HyperLinker augments the hypergraphs by perturbing hyperedges to simulate variations and noise, and introduces an invariant learning strategy. Extensive experiments conducted on four real-world datasets demonstrate the superiority of Multi-HyperLinker, achieving performance improvements of up to 19.80% in hit rate compared to existing HGNN-based methods. Additionally, it exhibits enhanced robustness on incomplete and noisy hypergraphs.

Multimodal hate detection, which aims to identify the harmful content online such as memes, is crucial for building a wholesome internet environment. Previous work has made enlightening exploration in detecting explicit hate remarks. However, most of their approaches neglect the analysis of implicit harm, which is particularly challenging as explicit text markers and demographic visual cues are often twisted or missing. The leveraged cross-modal attention mechanisms also suffer from the distributional modality gap and lack logical interpretability. To address these semantic gap issues, we propose TOT: a topology-aware optimal transport framework to decipher the implicit harm in memes scenario, which formulates the cross-modal aligning problem as solutions for optimal transportation plans. Specifically, we leverage an optimal transport kernel method to capture complementary information from multiple modalities. The kernel embedding provides a non-linear transformation ability to reproduce a kernel Hilbert space (RKHS), which reflects significance for eliminating the distributional modality gap. Moreover, we perceive the topology information based on aligned representations to conduct bipartite graph path reasoning. The newly achieved state-of-the-art performance on two publicly available benchmark datasets, together with further visual analysis, demonstrate the superiority of TOT in capturing implicit cross-modal alignment.

Weakly Supervised 3D Object Detection (WS3DOD) aims to perform 3D object detection with little reliance on 3D labels, which greatly reduces the cost of 3D annotations. In recent literature, the pseudo-label-based approach brings impressive performance, which generates 3D pseudo-labels from 2D bounding boxes. Despite their success, two key issues remain unresolved that reduce the quality of 3D pseudo-labels: 1) the existing local object locating algorithm can not capture complete clusters of points globally, and 2) the existing algorithm can not capture sparse points caused by the unevenly distributed points obtained by LiDAR cameras. Hence, we propose GAL, a Graph-induced Adaptive Learning algorithm, to generate 3D pseudo-labels. First, we propose the Cluster Locating algorithm based on the Minimum Spanning Tree (MST) to globally locate the objects, which can leverage the characteristic that points inside an object are compact while points between objects are discrete. Second, we propose a density-guided adaptive learning algorithm to optimise the Cluster Locating algorithm, named Cuboid Drift. Cuboid Drift considers the inhomogeneous distribution of reflected points on different reflective surfaces of LiDAR imaging. Finally, 3D pseudo-labels generated by GAL are leveraged to train 3D detectors. Extensive experiments on the challenging KITTI and DAIR-V2X-V dataset demonstrate that GAL without 3D labels can be comparable with strongly supervised approaches and outperforms the previous state-of-the-art WS3DOD methods. Moreover, our method saves 88% of the time spent on pseudo-label generation.

The label assignment problem is a core task in object detection, which mainly focuses on how to define the$positive/negative$samples during the training phase. Recent works have proved that label assignment is significant for performance improvement of the detector. In this article, we propose an exquisite strategy that can dynamically assign labels according samples' joint scores (classification and location). Moreover, our strategy can apply to both 2D and 3D monocular detectors. In our strategy, we formulate label assignment as an optimization problem. Concretely, we first calculate the classification and location costs of each sample, which are treated as points in a 2-D coordinate system. Then an optimal divider line that minimizes the sum of point-to-line distances is designed to separate the$positive/negative$samples. An iterative Genetic Algorithm is employed in acquiring the optimal solution. Furthermore, a GIoU auxiliary branch is devised to keep sample selection consistent during the training and testing phase. Benefitting from the non-maximum suppression (NMS) that utilizes the joint scores of classification and location, excellent detection performance is achieved. Extensive experiments conducted on MS COCO, PASCAL VOC (2D object detection), and KITTI (3D object detection) verify the effectiveness and universality of our proposed Optimal Partition Assignment (OPA).

N-ary relational knowledge base (KBs) embedding aims to map binary and beyond-binary facts into low-dimensional vector space simultaneously. Existing approaches typically decompose n-ary relational facts into subtuples (entity pairs, triples or quintuples, etc.), and they generally model n-ary relational KBs in Euclidean space. However, n-ary relational facts are semantically and structurally intact, decomposition leads to the loss of global information and undermines the semantical and structural integrity. Moreover, compared to the binary relational KBs, n-ary ones are characterized by more abundant and complicated hierarchy structures, which could not be well expressed in Euclidean space. To address the issues, we propose a gyro-polygon embedding approach to realize n-ary fact integrity keeping and hierarchy capturing, termed as PolygonE. Specifically, n-ary relational facts are modeled as gyro-polygons in the hyperbolic space, where we denote entities in facts as vertexes of gyro-polygons and relations as entity translocation operations. Importantly, we design a fact plausibility measuring strategy based on the vertex-gyrocentroid geodesic to optimize the relation-adjusted gyro-polygon. Extensive experiments demonstrate that PolygonE shows SOTA performance on all benchmark datasets, generalizability to binary data, and applicability to arbitrary arity fact. Finally, we also visualize the embedding to help comprehend PolygonE's awareness of hierarchies.