Langcai Cao

Community detection is a fundamental task in complex network analysis, aiming to identify closely connected subgraphs within the network. Recently, nonnegative matrix factorization (NMF)-based community detection methods have garnered significant attention due to their excellent clustering performance. However, most existing approaches are shallow and fail to fully capture the neighborhood information of nodes, which plays a critical role in node embedding learning. Considering the recent success of graph contrastive learning, its explicit structural constraint characteristics can effectively guide the model to learn embeddings useful for node clustering. To address the problems mentioned above, we propose a deep nonnegative matrix factorization model based on multiple contrastive constraints (CCDNMF). By incorporating the multi-neighborhood sampling method based on multiple constraints contrastive learning, this approach leverages node neighborhood features from different perspectives, capturing a rich set of local data distribution characteristics. This enables the learning of embeddings that preserve both global semantics and hierarchical local similarities, effectively integrating DNMF’s interpretability with the representational strengths of contrastive learning, and enhancing community discovery in complex networks. Extensive experiments are conducted on eleven real-world datasets that demonstrate the effectiveness and rationality of the proposed approach. The experimental results show that CCDNMF is superior to most existing methods.

Factorization machines (FMs) are widely employed as supervised predictors in collaborative recommendation. FMs can efficiently model second-order feature interactions through inner products, which proves beneficial for mitigating the negative effects of data sparsity. However, existing research has largely overlooked the potential correlations and attributes among features in FMs. These inherent relationships between features can enhance our understanding and facilitate the modeling of meaningful feature representations. To address this gap and capture intrinsic correlation information in the data, we propose a novel model named Feature Reweighting-based Factorization Machine (FRFM) in this paper. Specifically, we incorporate similarity into FM and quantify the strength of interactions between features using a similarity calculation method based on mutual information.We then introduce a feature reweighting strategy to effectively learn latent representations, ensuring that similar features exhibit comparable first-order weights and second-order embedding vectors based on their similarity. By assigning different weights to different feature pairs, FRFM adeptly captures the potential correlations and attributes among features within the model. Furthermore, FRFM can be seamlessly integrated into other models to enhance their performance. Extensive experiments conducted on six real-world datasets demonstrate the advantages of our proposed FRFM compared to state-of-the-art methods.

After the coronavirus disease 2019 (COVID-19) outbreak erupted, it swiftly spread globally and triggered a severe public health crisis in 2019. To contain the virus’s spread, several countries implemented various lockdown measures. As the governments faced this unprecedented challenge, understanding the impact of lockdown policies became paramount. The goal of addressing the pandemic crisis is to devise prudent policies that strike a balance between safeguarding lives and maintaining economic stability. Traditional mathematical and statistical models for studying virus transmission only offer macro-level predictions of epidemic development and often overlook individual variations’ impact, therefore failing to reflect the role of government decisions. To address this challenge, we propose an integrated framework that combines agent-based modeling (ABM) and deep Q-network (DQN) techniques. This framework enables a more comprehensive analysis and optimization of epidemic control strategies while considering real human behavior. We construct a pandemic simulator based on the ABM method, accurately simulating agents’ daily activities, interactions, and the dynamic spread of the virus. Additionally, we employ a data-driven approach and adjust the model through real statistical data to enhance its effectiveness. Subsequently, we integrated ABM into a decision-making framework using reinforcement learning techniques to explore the most effective strategies. In experiments, we validated the model’s effectiveness by simulating virus transmission across different countries globally. In this model, we obtained decision outcomes when governments focused on various factors. Our research findings indicate that our model serves as a valuable tool for decision-makers, enabling them to formulate prudent and rational policies.