We consider the web community detection problem by providing a cost function that, not only penalizes external connections, but also rewards the internal ones. Our formulation addresses limitations of cut-clustering and extends web communities to digraphs. The formulation is parametric, resulting in a hierarchy of communities that is representable in linear storage and computable in a linear number of maxflow computations. Experiments on synthetic and real-world datasets show that the proposed method can find better web communities and more densest subgraphs than previous formulations. Simple examples also show that it can return different and more meaningful communities compared to other formulations that are based on graph conductance, map equation and modularity score.

We address the challenge of identifying meaningful communities by proposing a model based on convex game theory and a measure of community strength. Many existing community detection methods fail to provide unique solutions, and it remains unclear how the solutions depend on initial conditions. Our approach identifies strong communities with a hierarchical structure, visualizable as a dendrogram, and computable in polynomial time using submodular function minimization. This framework extends beyond graphs to hypergraphs or even polymatroids. In the case when the model is graphical, a more efficient algorithm based on the max-flow min-cut algorithm can be devised. Though not achieving near-linear time complexity, the pursuit of practical algorithms is an intriguing avenue for future research. Our work serves as the foundation, offering an analytical framework that yields unique solutions with clear operational meaning for the communities identified.

The resistance to temper softening in low carbon martensite with its underlying origin, by microalloying of strong carbide-forming alloying elements (V, Nb and Ti) to an Fe-0.1C-1.5Mn-0.05Si (mass%) alloy, was investigated in this study. With similar hardness in as-quenched condition in all the alloys used, the hardness of tempered martensite is increased by V, Nb and Ti additions, particularly after treatment at higher temperature with longer time. The increment in hardness becomes larger by more amount of V addition, while with almost the same amount of microalloying additions, Nb and Ti provide larger strengthening than that of V. Atom probe measurements have revealed that a high density of nano-sized alloy carbides are formed in those alloys with V, Nb and Ti additions at 923 K, where large secondary hardening was observed. At 723 K, where there is some resistance to temper softening, however, almost no precipitation of V, Nb and Ti can be detected. The X-ray line profile analysis of the tempered alloys implies that the reduction in dislocation density during tempering is strongly retarded by V, Nb and Ti additions. This should be the major reason for their resistance to temper softening at relatively lower temperature, even without nano-precipitation of alloy carbides.

We consider the problem of identifying the source of a rumor in a network, given only a snapshot observation of infected nodes after the rumor has spread. Classical approaches, such as the maximum likelihood (ML) and joint maximum likelihood (JML) estimators based on the conventional Susceptible–Infectious (SI) model, exhibit degeneracy, failing to uniquely identify the source even in simple network structures. To address these limitations, we propose a generalized estimator that incorporates independent random observation times. To capture the structure of information flow beyond graphs, our formulations consider rate constraints on the rumor and the multicast capacities for cyclic polylinking networks. Furthermore, we develop forward elimination and backward search algorithms for rate-constrained source detection and validate their effectiveness and scalability through comprehensive simulations. Our study establishes a rigorous and scalable foundation on the infodemic source detection.

The exponentially growing content on the Internet includes online publications, scientific news, and other expert websites. It brings a formidable challenge to extracting pertinent answers from such vast information. We proposed a deep neural network model based on BiLSTM(Bi-directional Long Short-Term Memory) and ALBERT(A Lite BERT for Self-Supervised Learning of Language Representations) for Chinese question-answering in the scientific context. Our model significantly enhances the generalization capabilities of the transformer-based model for question answering. The character extraction and word embedding modules are designed for tackling intricate science-related queries, swiftly assimilating knowledge from cutting-edge scientific literature, and contributing to constructing a comprehensive scientific knowledge graph. Our model is characterized by compactness, swift execution, and satisfactory accuracy. It has been meticulously fine-tuned, emphasizing multi-sentence coherence augmented by attention mechanisms, thereby ensuring robust scalability. Empirical evaluations on the LCQMC and XNLI datasets demonstrate that our approach surpasses the performance results of BERT and ALBERT, showing the potential for large-scale Chinese question-matching problems.