Complexity of social media in the era of generative AI.

The article presents a perspective on how the emerging generative AI technology can shape social media and the new challenges in studying social media in this generative.

A generalized simplicial model and its application.

Higher-order structures, consisting of more than two individuals, provide a new perspective to reveal the missed non-trivial characteristics under pairwise networks. Prior works have researched various higher-order networks, but research for evaluating the effects of higher-order structures on network functions is still scarce. In this paper, we propose a framework to quantify the effects of higher-order structures (e.

Transcriptomics of Leaf Development in the Endangered Dioecious : Molecular Basis Underpinning Specialized Metabolism Genes.

, a dioecious tree native to China, is recognized not only for its status as an at-risk species but also for its potential in therapeutic applications courtesy of its bioactive compounds. However, the genetic underpinnings of its leaf development and compound biosynthesis are not well documented. Our study aims to bridge this knowledge gap through comparative transcriptomics, analyzing gene expression through different leaf maturation stages.

Network Higher-Order Structure Dismantling.

Diverse higher-order structures, foundational for supporting a network's "meta-functions", play a vital role in structure, functionality, and the emergence of complex dynamics. Nevertheless, the problem of dismantling them has been consistently overlooked. In this paper, we introduce the concept of dismantling higher-order structures, with the objective of disrupting not only network connectivity but also eradicating all higher-order structures in each branch, thereby ensuring thorough functional paralysis.

Integrative analysis of microbiota and metabolomics in chromium-exposed silkworm () midguts based on 16S rDNA sequencing and LC/MS metabolomics.

The gut microbiota, a complex ecosystem integral to host wellbeing, is modulated by environmental triggers, including exposure to heavy metals such as chromium. This study aims to comprehensively explore chromium-induced gut microbiota and metabolomic shifts in the quintessential lepidopteran model organism, the silkworm (). The research deployed 16S rDNA sequence analysis and LC/MS metabolomics in its experimental design, encompassing a control group alongside low (12 g/kg) and high (24 g/kg) feeding chromium dosing regimens.

Hyper-Null Models and Their Applications.

Null models are crucial tools for investigating network topological structures. However, research on null models for higher-order networks is still relatively scarce. In this study, we introduce an innovative method to construct null models for hypergraphs, namely the hyperedge swapping-based method.

Higher-order link prediction via local information.

Link prediction has been widely studied as an important research direction. Higher-order link prediction has gained, in particular, significant attention since higher-order networks provide a more accurate description of real-world complex systems. However, higher-order networks contain more complex information than traditional pairwise networks, making the prediction of higher-order links a formidable challenging task.

Predicting the Popularity of Information on Social Platforms without Underlying Network Structure.

The ability to predict the size of information cascades in online social networks is crucial for various applications, including decision-making and viral marketing. However, traditional methods either rely on complicated time-varying features that are challenging to extract from multilingual and cross-platform content, or on network structures and properties that are often difficult to obtain. To address these issues, we conducted empirical research using data from two well-known social networking platforms, WeChat and Weibo.

Toward Detecting Previously Undiscovered Interaction Types in Networked Systems.

Studying networked systems in a variety of domains, including biology, social science, and Internet of Things, has recently received a surge of attention. For a networked system, there are usually multiple types of interactions between its components, and such interaction-type information is crucial since it always associated with important features. However, some interaction types that actually exist in the network may not be observed in the metadata collected in practice.

The simple regularities in the dynamics of online news impact.

Unlabelled: Online news can quickly reach and affect millions of people, yet we do not know yet whether there exist potential dynamical regularities that govern their impact on the public. We use data from two major news outlets, BBC and New York Times, where the number of user comments can be used as a proxy of news impact. We find that the impact dynamics of online news articles does not exhibit popularity patterns found in many other social and information systems.

History-dependent percolation on multiplex networks.

The structure of interconnected systems and its impact on the system dynamics is a much-studied cross-disciplinary topic. Although various critical phenomena have been found in different models, study of the connections between different percolation transitions is still lacking. Here we propose a unified framework to study the origins of the discontinuous transitions of the percolation process on interacting networks.

Relationship Between Body Mass Index and Recurrence/Anal Fistula Formation Following Initial Operation for Anorectal Abscess.

BACKGROUND The association between body mass index (BMI) and recurrence of anorectal abscess remains controversial. This study investigated the exact relationship between BMI and anorectal abscess recurrence or anal fistula formation following initial surgery. MATERIAL AND METHODS This was a retrospective registry-based study conducted at the First Affiliated Hospital of Guizhou University of Chinese Medicine.

Totally homogeneous networks.

In network science, the non-homogeneity of node degrees has been a concerning issue for study. Yet, with today's modern web technologies, the traditional social communication topologies have evolved from node-central structures into online cycle-based communities, urgently requiring new network theories and tools. Switching the focus from node degrees to network cycles could reveal many interesting properties from the perspective of totally homogenous networks or sub-networks in a complex network, especially basic simplexes (cliques) such as links and triangles.

Deep Collaborative Filtering for Prediction of Disease Genes.

Accurate prioritization of potential disease genes is a fundamental challenge in biomedical research. Various algorithms have been developed to solve such problems. Inductive Matrix Completion (IMC) is one of the most reliable models for its well-established framework and its superior performance in predicting gene-disease associations.

Link Prediction in Bipartite Nested Networks.

Real networks typically studied in various research fields-ecology and economic complexity, for example-often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link prediction in complex networks, which aims at identifying likely candidates for missing links. We find that a new method that takes network nestedness into account outperforms well-established link-prediction methods not only when the input networks are sufficiently nested, but also for networks where the nested structure is imperfect.

Prediction of potential disease-associated microRNAs using structural perturbation method.

Motivation: The identification of disease-related microRNAs (miRNAs) is an essential but challenging task in bioinformatics research. Similarity-based link prediction methods are often used to predict potential associations between miRNAs and diseases. In these methods, all unobserved associations are ranked by their similarity scores.

Predicting missing links and identifying spurious links via likelihood analysis.

Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network's probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network.

Efficient network disintegration under incomplete information: the comic effect of link prediction.

The study of network disintegration has attracted much attention due to its wide applications, including suppressing the epidemic spreading, destabilizing terrorist network, preventing financial contagion, controlling the rumor diffusion and perturbing cancer networks. The crux of this matter is to find the critical nodes whose removal will lead to network collapse. This paper studies the disintegration of networks with incomplete link information.

The H-index of a network node and its relation to degree and coreness.

Identifying influential nodes in dynamical processes is crucial in understanding network structure and function. Degree, H-index and coreness are widely used metrics, but previously treated as unrelated. Here we show their relation by constructing an operator , in terms of which degree, H-index and coreness are the initial, intermediate and steady states of the sequences, respectively.

Empirical Studies on the Network of Social Groups: The Case of Tencent QQ.

Background: Participation in social groups are important but the collective behaviors of human as a group are difficult to analyze due to the difficulties to quantify ordinary social relation, group membership, and to collect a comprehensive dataset. Such difficulties can be circumvented by analyzing online social networks.

Methodology/principal Findings: In this paper, we analyze a comprehensive dataset released from Tencent QQ, an instant messenger with the highest market share in China.

Toward link predictability of complex networks.

The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability.

Identifying influential nodes in large-scale directed networks: the role of clustering.

Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node's neighbors but do not directly make use of the interactions among it's neighbors.

Spreading in online social networks: the role of social reinforcement.

Some epidemic spreading models are usually applied to analyze the propagation of opinions or news. However, the dynamics of epidemic spreading and information or behavior spreading are essentially different in many aspects. Centola's experiments [Science 329, 1194 (2010)] on behavior spreading in online social networks showed that the spreading is faster and broader in regular networks than in random networks.