Leveraging the diversity and quantity of data provided by various graph-structured data augmentations while preserving intrinsic semantic information is challenging. Additionally, successive layers in graph neural network (GNN) tend to produce more similar node embeddings, while graph contrastive learning aims to increase the dissimilarity between negative pairs of node embeddings. This inevitably results in a conflict between the message-passing mechanism (MPM) of GNNs and the contrastive learning (CL) of negative pairs via intraviews. In this paper, we propose a conditional distribution learning (CDL) method that learns graph representations from graph-structured data for semisupervised graph classification. Specifically, we present an end-to-end graph representation learning model to align the conditional distributions of weakly and strongly augmented features over the original features. This alignment enables the CDL model to effectively preserve intrinsic semantic information when both weak and strong augmentations are applied to graph-structured data. To avoid the conflict between the MPM and the CL of negative pairs, positive pairs of node representations are retained for measuring the similarity between the original features and the corresponding weakly augmented features. Extensive experiments with several benchmark graph datasets demonstrate the effectiveness of the proposed CDL method.

How to simultaneously locate multiple global peaks and achieve certain accuracy on the found peaks are two key challenges in solving multimodal optimization problems (MMOPs). In this article, a landscape-aware differential evolution (LADE) algorithm is proposed for MMOPs, which utilizes landscape knowledge to maintain sufficient diversity and provide efficient search guidance. In detail, the landscape knowledge is efficiently utilized in the following three aspects. First, a landscape-aware peak exploration helps each individual evolve adaptively to locate a peak and simulates the regions of the found peaks according to search history to avoid an individual re-locating an already found peak. Second, a landscape-aware peak distinction distinguishes whether an individual locates a new global peak, a new local peak, or an already found peak. Accuracy refinement can thus only be conducted on the global peaks to enhance the search efficiency. Third, a landscape-aware reinitialization specifies the initial position of an individual adaptively according to the distribution and distinction of the found peaks, which helps explore more peaks. The experiments are conducted on the widely-used benchmark MMOPs and multimodal nonlinear equation system problems. Experimental results show that LADE obtains generally better or competitive performance compared with seven well-performing recent algorithms and four winner algorithms in the IEEE CEC competitions for multimodal optimization.

Graph representation learning is fundamental for analyzing graph-structured data. Exploring invariant graph representations remains a challenge for most existing graph representation learning methods. In this paper, we propose a cross-view graph consistency learning (CGCL) method that learns invariant graph representations for link prediction. First, two complementary augmented views are derived from an incomplete graph structure through a coupled graph structure augmentation scheme. This augmentation scheme mitigates the potential information loss that is commonly associated with various data augmentation techniques involving raw graph data, such as edge perturbation, node removal, and attribute masking. Second, we propose a CGCL model that can learn invariant graph representations. A cross-view training scheme is proposed to train the proposed CGCL model. This scheme attempts to maximize the consistency information between one augmented view and the graph structure reconstructed from the other augmented view. Furthermore, we offer a comprehensive theoretical CGCL analysis. This paper empirically and experimentally demonstrates the effectiveness of the proposed CGCL method, achieving competitive results on graph datasets in comparisons with several state-of-the-art algorithms.

Effective expert selection is an important guarantee for academic evaluation. Recently, graph structure learning (GSL) have been used to model the complex relationships between candidates and experts in both professional fields and avoidance strategies. Most existing GSL models aim to learn the structure of homogeneous graph. However, they cannot learn the structure of heterogeneous graph (HG) because they ignore the complex relation attributes in HG. To fill this gap, we propose a relation enhanced heterogeneous graph structure learning (RE-HGSL) model that refines HG structure and learns node and relation representations simultaneously for expert selection in academic evaluation. The main idea of RE-HGSL is refining the HG structure by reconstructing edges through the similarity of relation triplet$<$head node, relation, tail node$>$. Specifically, we first set relation representation vector for each kind of relation to capture multiple relation attributes and learn node and relation representations to preserve multiple semantic information. Then, we construct multiple feature graphs where the edges are constructed according to the similarity of the relation triplet. Finally, the refined graph structure is generated by aggregating the original graph and the feature graphs, which maintains not only the part of original vital structure but also the refined edges.

The acceptance of academic papers involves a complex peer-review process that requires substantial human and material resources and is susceptible to biases. With advancements in deep learning technologies, researchers have explored automated approaches for assessing paper acceptance. Existing automated academic paper rating methods primarily rely on the full content of papers to estimate acceptance probabilities. However, these methods are often inefficient and introduce redundant or irrelevant information. Additionally, while Bert can capture general semantic representations through pretraining on large-scale corpora, its performance on the automatic academic paper rating (AAPR) task remains suboptimal due to discrepancies between its pretraining corpus and academic texts. To address these issues, this study proposes LMCBert, a model that integrates large language models (LLMs) with momentum contrastive learning (MoCo). LMCBert utilizes LLMs to extract the core semantic content of papers, reducing redundancy and improving the understanding of academic texts. Furthermore, it incorporates MoCo to optimize Bert training, enhancing the differentiation of semantic representations and improving the accuracy of paper acceptance predictions. Empirical evaluations demonstrate that LMCBert achieves effective performance on the evaluation dataset, supporting the validity of the proposed approach. The code and data used in this article are publicly available at https://github.com/iioSnail/LMCBert.

In the current wave of technological innovation, the scientific and technological evaluation system not only plays a crucial role in assessing scientific research achievements but also serves as a driving force for advancing scientific research. To enhance the effectiveness and accuracy of peer review in the scientific and technological evaluation system, an integrating fuzzy C-Means clustering algorithm and similarity analysis method is proposed. In this methodology, the fuzzy C-Means clustering algorithm is first applied to conduct cluster analysis on expert feature attribute. Second, the recursive feature elimination feature selection is used to select the important features for enhancing the model generalization capability. The Gower distance of key features between experts is calculated to determine the similarity between experts, and the association features among experts are mined. Finally, an expert credibility prediction model is constructed based on a feedforward neural network. A real-world expert credibility dataset was used to validate the effectiveness and feasibility of the proposed method. The experimental results indicated that integrating the fuzzy C-Means clustering algorithm and similarity association is an effective way to improve the model prediction performance. This can be considered a promising solution for the scientific and technological evaluation.

Incomplete multiview clustering (IMVC) optimally integrates complementary information within incomplete multiview data to improve clustering performance. Several one-step graph-based methods show great potential for IMVC. However, the low-rank structures of similarity graphs are neglected at the initialization stage of similarity graph construction. Moreover, further investigation into complementary information integration across incomplete multiple views is needed, particularly when considering the low-rank structures implied in high-dimensional multiview data. In this paper, we present one-step adaptive graph learning (OAGL) that adaptively performs spectral embedding fusion to achieve clustering assignments at the clustering indicator level. We first initiate affinity matrices corresponding to incomplete multiple views using spare representation under two constraints, i.e., the sparsity constraint on each affinity matrix corresponding to an incomplete view and the degree matrix of the affinity matrix approximating an identity matrix. This approach promotes exploring complementary information across incomplete multiple views. Subsequently, we perform an alignment of the spectral block-diagonal matrices among incomplete multiple views using low-rank tensor learning theory. This facilitates consistency information exploration across incomplete multiple views. Furthermore, we present an effective alternating iterative algorithm to solve the resulting optimization problem. Extensive experiments on benchmark datasets demonstrate that the proposed OAGL method outperforms several state-of-the-art approaches.

Crowdsourcing contests have become an important method for individuals and organizations to solve complex problems by obtaining innovative solutions from external participants. As the number of participants continues to grow, the likelihood of undesirable outcomes increases, posing a great requirement for effective community detection algorithms. To provide platform owners with actionable and timely management strategies, this article proposes a multimodule-based dynamic community detection (MDCD) algorithm to facilitate the achievement of efficient, high-quality, and sustainable innovation. The MDCD algorithm uses a multimodule task learning framework containing four different modules, including heterogeneous temporal aggregation (HTA), representation reconstruction (RR), link prediction (LP), and node clustering (NC) modules, to gradually detect the community structure accurately. First, the HTA module obtains the initial node representation by capturing both spatial heterogeneity and temporal dependencies. Second, the RR module considers reconstructed topology and node attribute information to update the node representation via an encoder–decoder collaboration mechanism. Third, the LP module further optimizes the node representation by exploiting the predicted graph links, which helps increase the accuracy of community detection. Finally, the NC module leverages two metric learning methods to optimize a learnable clustering process based on the predicted node presentations, which helps platform owners achieve comprehensive results across multiple dimensions of innovation performance. The experimental results from real-world crowdsourcing platforms indicate that MDCD shows effectiveness in simultaneously improving the multidimensional innovation performance of crowdsourcing platforms and increasing solver engagement.