Research on human-animal interaction has skyrocketed in the last decade. Rapid urbanization has led scientists to investigate its impact on several species living in the vicinity of humans. Domesticated dogs (Canis lupus familiaris) are one such species that interact with humans and are also called man’s best friend. However, when it comes to the free-ranging population of dogs, interactions become quite complicated. Unfortunately, studies regarding free-ranging dog-human interactions are limited even though the majority of the world’s dog population (...) is free-ranging. In this study, we observed twelve groups of free-ranging dogs in their natural habitat, the streets. We divided the study areas into two zones, namely – intermediate and high flux, based on human activity or movement. Six groups from each of the zones were observed mostly during daytime. We used social network analysis (SNA) to explore the dog-dog (intraspecific) and dog-human (interspecific) interactions. It was surprising to observe higher instances of interspecific interactions than the intraspecific ones, irrespective of the zones. Humans, in significantly higher occasions, initiated both positive and negative interactions in comparison to dogs. Our findings conclude that humans are a crucial part of the interaction network of Indian free-ranging dogs. (shrink)

This work studies the interaction networks that arise on Twitter in relation to such a relevant topic as climate change. We detected that the largest connected component of these networks presents low values of average degree and betweenness, as well as a small diameter compared to the total number of nodes in the network. The largest connected component of retweeting and quoting networks also exhibits very low negative assortativity. The quoting and retweeting networks have a more (...) hierarchical structure than the replying network. We also find that the process of emergence of new links in the interaction networks can be properly modeled through a Support Vector Machine model using the embeddings provided by the Node2Vec algorithm. A Random Forest model using certain similarity measures as explanatory variables between nodes also provides high accuracy. In addition, we analyze the communities existing in each interaction network by means of the Louvain method. The cumulative probability distributions of hashtags per community are also examined. (shrink)

The Internet and other interactive networks are diffusing across the globe at rates that vary from country to country. Typically, economic and market structure variables are used to explain these differences. The addition of culture to these variables will provide a more robust understanding of the differences in Internet and interactive network diffusion. Existing analyses that identify culture as a predictor of diffusion do not adequately specify the dimensions of culture and their impacts.This paper presents a set of propositions (...) to be used in analyses of the impact of culture on the diffusion of interactive networks. The propositions were developed using cultural constructs presented by Hofstede (1991), Herbig (1994) and Hall and Hall (1987). Diffusion of innovations theory and critical mass theory provide the theoretical base. The development of the propositions resulted from a close examination of the theories for relationships mediated by culture. The resulting propositions use cultural variables in relationships established by the theories. It is hoped that the propositions will serve as a starting point for future research in the area of cultural influences on the diffusion of interactive networks. (shrink)

We are currently witnessing the emergence of new forms of collective identities and a redefinition of the old ones through networked digital interactions, and these can be explicitly measured and analyzed. We distinguish between three major trends on the development of the concept of identity in the social realm: (1) an essentialist sense (based on conditions and properties shared by members of a group), (2) a representational or ideational sense (based on the application of categories by oneself or others), (...) and (3) a relational and interactional sense (based on interaction processes between actors and their environments). The interactional approach aligns with current empirical and methodological progress in social network analysis. Moreover, it has been argued that, within the network society, the notion of collective identity (Melucci, 1995) in the political field must be rethought as technologically mediated and interactive. We suggest that collective identities should be understood as recurrent, cohesive, and coordinated communicative interaction networks. We here propose that such identities can be depicted by: (a) mapping and filtering a relevant interaction network, (b) delimiting a set of communities, (c) determining the strongly connected component(s) of such communities (the core identity) in a directed graph, and (d) defining the identity audiences and sources within the community. This technical graph–theoretical characterization is explained and justified in... (shrink)

Comprehensive analysis of proteins to evaluate their genetic diversity, study their differences, and respond to the tensions is the main subject of an interdisciplinary field of study called proteomics. The main objective of the proteomics is to detect and quantify proteins and study their post-translational modifications and interactions using protein chemistry, bioinformatics, and biology. Any disturbance in proteins interactive network can act as a source for biological disorders and various diseases such as Alzheimer and cancer. Most current computational methods (...) for discovering protein complexes are usually based on specific topological characteristics of protein-protein networks. To identify the protein complexes, in this paper, we, first, present a new encoding method to represent solutions; we then propose a new clustering algorithm based on the genetic algorithm, named PPI-GA, employing a new multiobjective quality function. The proposed algorithm is evaluated on two gold standard and real-world datasets. The result achieved demonstrates that the proposed algorithm can detect important protein complexes, and it provides more accurate results compared with state-of-the-art protein complex identification algorithms. (shrink)

A general framework by which dynamic interactions within a protein will promote the necessary series of structural changes, or “conformational cycle,” required for function is proposed. It is suggested that the free‐energy landscape of a protein is biased toward this conformational cycle. Fluctuations into higher energy, although thermally accessible, conformations drive the conformational cycle forward. The amino acid interaction network is defined as those intraprotein interactions that contribute most to the free‐energy landscape. Some network connections are consistent in (...) every structural state, while others periodically change their interaction strength according to the conformational cycle. It is reviewed here that structural transitions change these periodic network connections, which then predisposes the protein toward the next set of network changes, and hence the next structural change. These concepts are illustrated by recent work on tryptophan synthase. Disruption of these dynamic connections may lead to aberrant protein function and disease states. (shrink)

Business Ethics, the Environment &Responsibility, Volume 32, Issue 3, Page 877-881, July 2023.

The aberrations of a gene can influence it and the functions of its neighbour genes in gene interaction network, leading to the development of carcinogenesis of normal cells. In consideration of gene interaction network as a complex network, previous studies have made efforts on the driver attribute filling of genes via network properties of nodes and network propagation of mutations. However, there are still obstacles from problems of small size of cancer samples and the existence of drivers without property of (...) network neighbours, limiting the discovery of cancer driver genes. To address these obstacles, we propose an efficient modularity subspace based concept learning model. Our model can overcome the curse of dimensionality due to small samples via dimension reduction in the task of attribute concept learning and explore the features of genes through modularity subspace beyond the network neighbours. The evaluation analysis also demonstrates the superiority of our model in the task of driver attribute filling on two gene interaction networks. Generally, our model shows a promising prospect in the application of interaction network analysis of tumorigenesis. (shrink)

In cognitive neuroscience, dissociating the brain networks that ing—has thus become one of the best empirical situations subtend conscious and nonconscious memories constitutes a through which to study the mechanisms of implicit learning, very complex issue, both conceptually and methodologically.

An algebraic interpretation of multigraph networks is introduced in relation to conscious experience, brain and body. These multigraphs have the ability to merge by an associative binary operator \(\odot \), accounting for biological composition. We also study a mathematical formulation of splitting layers, resulting in a formal analysis of the transition from conscious to non-conscious activity. From this construction, we recover core structures for conscious experience, dynamical content and causal constraints that conscious interactions may impose. An important result (...) is the prediction of structural topological changes after conscious interactions. These results may inspire further use of formal mathematics to describe and predict new features of conscious experience while aligning well with formal tries to mathematize phenomenology, phenomenological tradition and applications to artificial consciousness. (shrink)

Changes in the abundance of protein and RNA molecules can impair the formation of complexes in the cell leading to toxicity and death. Here we exploit the information contained in protein, RNA and DNA interaction networks to provide a comprehensive view of the regulation layers controlling the concentration‐dependent formation of assemblies in the cell. We present the emerging concept that RNAs can act as scaffolds to promote the formation ribonucleoprotein complexes and coordinate the post‐transcriptional layer of gene regulation. We (...) describe the structural and interaction network properties that characterize the ability of protein and RNA molecules to interact and phase separate in liquid‐like compartments. Finally, we show that presence of structurally disordered regions in proteins correlate with the propensity to undergo liquid‐to‐solid phase transitions and cause human diseases. Also see the video abstract here https://youtu.be/kfpqibsNfS0. (shrink)

The emerging area of network biology is seeking to provide insights into organizational principles of life. However, despite significant collaborative efforts, there is still typically a weak link between biological and computational scientists and a lack of understanding of the research issues across the disciplines. This results in the use of simple computational techniques of limited potential that are incapable of explaining these complex data. Hence, the danger is that the community might begin to view the topological properties of network (...) data as mere statistics, rather than rich sources of biological information. A further danger is that such views might result in the imposition of scientific doctrines, such as scale-free-centric (on the modeling side) and genome-centric (on the biological side) opinions onto this area. Here, we take a graph-theoretic perspective on protein-protein interaction networks and present a high-level overview of the area, commenting on possible challenges ahead. (shrink)

The distribution of cultural variants in a population is shaped by both neutral evolutionary dynamics and by selection pressures. The temporal dynamics of social network connectivity, that is, the order in which individuals in a population interact with each other, has been largely unexplored. In this paper, we investigate how, in a fully connected social network, connectivity dynamics, alone and in interaction with different cognitive biases, affect the evolution of cultural variants. Using agent‐based computer simulations, we manipulate population connectivity dynamics (...) (early, mid, and late full‐population connectivity); content bias, or a preference for high‐quality variants; coordination bias, or whether agents tend to use self‐produced variants (egocentric bias), or to switch to variants observed in others (allocentric bias); and memory size, or the number of items that agents can store in their memory. We show that connectivity dynamics affect the time‐course of variant spread, with lower connectivity slowing down convergence of the population onto a single cultural variant. We also show that, compared to a neutral evolutionary model, content bias accelerates convergence and amplifies the effects of connectivity dynamics, while larger memory size and coordination bias, especially egocentric bias, slow down convergence. Furthermore, connectivity dynamics affect the frequency of high‐quality variants (adaptiveness), with late connectivity populations showing bursts of rapid change in adaptiveness followed by periods of relatively slower change, and early connectivity populations following a single‐peak evolutionary dynamic. We evaluate our simulations against existing data collected from previous experiments and show how our model reproduces the empirical patterns of convergence. (shrink)

Some properties of cognitive networks are discussed in the article in the context of the modern achievements of the network science. It is the study in network structures and their surprising properties that gives a new impetus to the development of the theory of complex systems. The analysis of cognitive processes from the point of view of the network structures that arise in them not only fits with such concepts already existing in cognitive science and epistemology, as cognitive niches, (...) cognitive maps, cognitive coherence, etc.), but also brings some new aspects to the understanding of interactivity, intersubjectivity, synergy in cognition and creative activities, empathy. (shrink)

Some thoughts on the nature of networks, the prevalence of emotional experiences, and the importance of new questions.

Artificial Intelligence (AI) and Philosophy of Science share a fundamental problem—that of understanding causality. Bayesian network techniques have recently been used by Judea Pearl in a new approach to understanding causality and causal processes (Pearl, 2000). Pearl’s approach has great promise, but needs to be supplemented with an explicit account of causal interaction. Thus far, despite considerable interest, philosophy has provided no useful account of causal interaction. Here we provide one, employing the concepts of Bayesian networks. With it we (...) demonstrate the failure of one of philosophy’s more sophisticated attempts to deal with the concept of causal interaction, that of Ellery Eells’ Probabilistic Causality (1991). (shrink)

Online interactions have become major channels for people to obtain and disseminate information during the new normal of COVID-19, which can also be a primary platform for rumor propagation. There are many complex psychological reasons for spreading rumors, but previous studies have not fully analyzed this problem from the perspective of the interaction between official institutions and influential users. The purpose of this study is to determine optimal strategies for official institutions considering the impact of two different influential user (...) types by designing two game-theoretic models, namely “Rumor Clarification and Interaction Model” and “Rumor Verification and Interaction Model,” which can, respectively decide whether to clarify and when to clarify. The results of this article show that clarification strategies can be decided according to the characteristics of rumors and the influential user’s reactions. Meanwhile, publishing verified information prevents trolls’ “loophole advantages” and prevents reputed personalities from spreading false information due to the vague authenticity of rumors. Results also show that the verification strategy is limited by cost, period, and verification index. (shrink)

Computer-mediated interaction on the Internet provides new opportunities to examine the links between reputation, risk, and the development of trust between individuals who engage in various types of exchange. In this article, we comment on the application of experimental sociological research to different types of computer-mediated social interactions, with particular attention to the emergence of what we call ‘trust networks’ (networks of those one views as trustworthy). Drawing on the existing categorization systems that have been used in (...) experimental social psychology, we relate the various forms of computer-mediated exchange to selected findings from experimental research. We develop a simple typology based on the intersection of random versus fixed-partner social dilemma games, and repeated versus one-shot interaction situations. By crossing these two types of social dilemma games and two types of interaction situations, we show that many forms of Internet exchange can be categorized effectively into four mutually exclusive categories. The resulting classification system helps to integrate the existing research on t rust in experimental social psychology with the emerging field of computer-mediated exchange. (shrink)

During the last decade, the interaction based models have received increased attention in economics, mainly with the recognition that modeling aggregate patterns of behavior requires viewing individuals in their social environments continuously in interaction with each other. The existing literature suggests that statistical mechanics tools can be useful to model interactions among economic agents. In addition to statistical mechanics, the network approach has also gained popularity, as is evident in the rising attention attributed to small world models and scale (...) free network topologies. These developments point to the fact that interdisciplinary research in economics, mainly using the tools of physics has accelerated to a great extent. In this paper, we review the analogies made between molecules and economic agents in statistical mechanics models, as they are utilized in economics literature. We perform a simulation study by using the small world model and statistical mechanics, to demonstrate the influence of network structure in the spreading of organizational forms, in particular the research collaboration decisions of firms. (shrink)

Here, we present some experiments of non-cooperative games of network formation based on Bala and Goyal (Econometrica 68:1181–1229, 2000 ). We have looked at the one-way and the two-way flow models, each for high and low link costs. The models come up with both multiple equilibria and coordination problems. We conducted the experiments under various conditions which allowed for repeated interactions between subjects. We found that coordination on non-empty Strict Nash equilibria was not an easy task to achieve, even (...) in the one-way model where the Strict Nash equilibria are wheels. We found some evidence of convergence to equilibrium networks through learning dynamics, while we found no effect of salient labels to help coordination. The evidence on learning behavior provides support for subjects that were choosing strategies in line with various learning rules, principally Reinforcement and Fictitious Play. (shrink)

In this paper, an in-depth study of interactive visual communication of network topology through non-line-of-sight congestion control algorithms is conducted to address the real-time routing problem of adapting to dynamic topologies, and a delay-constrained stochastic routing algorithm is proposed to enable packets to reach GB within the delay threshold in the absence of end-to-end delay information while improving network throughput and reducing network resource consumption. The algorithm requires each sending node to select an available relay set based on the location (...) of its neighbor nodes and channel state and computes transfer probabilities for each node in the relay set combining the remaining delay of the packet with the distance from the relay node to GB. Based on the obtained transfer probability and local channel state, the sending node passes the packet to the relay node. The convergence of the algorithm is proved and its performance is verified by simulation. The first part of the algorithm is based on the greedy algorithm to deploy and locate the network flying platform nodes with the goal of efficient coverage of the network flying platform nodes, considering the ground base station services. As the delay on each link varies due to the change of channel state, the source and relay nodes asynchronously update the data generation rate and the pairwise parameters based on the received local information and use the obtained optimal values to pass the packets to GB. (shrink)

Somatic complementation by fusion of two mutant cells and mixing of their cytoplasms occurs when the genetic defect of one fusion partner is cured by the functional gene product provided by the other. We have found that complementation of mutational defects in the network mediating stimulus‐induced commitment and sporulation of Physarum polycephalum may reflect time‐dependent changes in the signaling state of its molecular building blocks. Network perturbation by fusion of mutant plasmodial cells in different states of activation, and the time‐resolved (...) analysis of somatic complementation effects can be used to systematically probe network structure and dynamics. Time‐resolved somatic complementation quantitatively detects regulatory interactions between the functional modules of a network, independent of their biochemical composition or subcellular localization, and without being limited to direct physical interactions. BioEssays 25:950–960, 2003. © 2003 Wiley Periodicals, Inc. (shrink)

Lindquist et al. provide a convincing case against what they call the locationist account of emotion. Their quantitative approach elegantly illustrates the shortcomings of this still-entrenched viewpoint. Here, I discuss how a network perspective will advance our understanding of structure-function mappings in general, and the relationship between emotion and cognition in the brain.

Artificial Intelligence (AI) and Philosophy of Science share a fundamental problem—understanding causality. Bayesian networks have recently been used by Judea Pearl in a new approach to understanding causality (Pearl, 2000). Part of understanding causality is understanding causal interaction. Bayes nets can represent any degree of causal interaction, and researchers normally try to limit interactions, usually by replacing the full CPT with a noisy-OR function. But we show that noisy-OR and another common model are merely special cases of the (...) general linear systems definition of noninteraction. However, they apply in different situations, and we can measure the degree of causal interaction relative to any such model. (shrink)

Emotional design is an important development trend of interaction design. Emotional design in products plays a key role in enhancing user experience and inducing user emotional resonance. In recent years, based on the user's emotional experience, the design concept of strengthening product emotional design has become a new direction for most designers to improve their design thinking. In the emotional interaction design, the machine needs to capture the user's key information in real time, recognize the user's emotional state, and use (...) a variety of clues to finally determine the appropriate user model. Based on this background, this research uses a deep learning mechanism for more accurate and effective emotion recognition, thereby optimizing the design of the interactive system and improving the user experience. First of all, this research discusses how to use user characteristics such as speech, facial expression, video, heartbeat, etc., to make machines more accurately recognize human emotions. Through the analysis of various characteristics, the speech is selected as the experimental material. Second, a speech-based emotion recognition method is proposed. The mel-Frequency cepstral coefficient of the speech signal is used as the input of the improved long and short-term memory network. To ensure the integrity of the information and the accuracy of the output at the next moment, ILSTM makes peephole connections in the forget gate and input gate of LSTM, and adds the unit state as input data to the threshold layer. The emotional features obtained by ILSTM are input into the attention layer, and the self-attention mechanism is used to calculate the weight of each frame of speech signal. The speech features with higher weights are used to distinguish different emotions and complete the emotion recognition of the speech signal. Experiments on the EMO-DB and CASIA datasets verify the effectiveness of the model for emotion recognition. Finally, the feasibility of emotional interaction system design is discussed. (shrink)

This commentary focuses on how the large-scale cortical dynamics described in Nunez's target article are related to various phenomena at different scales, both spatial and temporal, in particular, how the brain dynamics measured with EEG could relate to (i) experience and mental state, (ii) neuromodulatory effects, and (iii) spontaneous firing and autogenerated electromagnetic effects.

All the conserved detailed results of evolution stored in DNA must be read, transcribed, and translated via an RNAmediated process. This is required for the development and growth of each individual cell. Thus, all known living organisms fundamentally depend on these RNA-mediated processes. In most cases, they are interconnected with other RNAs and their associated protein complexes and function in a strictly coordinated hierarchy of temporal and spatial steps (i.e., an RNA network). Clearly, all cellular life as we know it (...) could not function without these key agents of DNA replication, namely rRNA, tRNA, and mRNA. Thus, any definition of life that lacks RNA functions and their networks misses an essential requirement for RNA agents that inherently regulate and coordinate (communicate to) cells, tissues, organs, and organisms. The precellular evolution of RNAs occurred at the core of the emergence of cellular life and the question remained of how both precellular and cellular levels are interconnected historically and functionally. RNA-networks andRNA-communication can interconnect these levels.With the reemergence of virology in evolution, it became clear that communicating viruses and subviral infectious genetic parasites are bridging these two levels by invading, integrating, coadapting, exapting, and recombining constituent parts in host genomes for cellular requirements in gene regulation and coordination aims. Therefore, a 21st century understanding of life is of an inherently social process based on communicating RNA networks, in which viruses and cells continuously interact. (shrink)

Niels Kaj Jerne has proposed the “immune network theory” of interactions among anti‐idiotypic antibodies, able to interfere with humoral responses to certain antigens. After the occurrence of the primary generation of antibodies, against an antigenic epitope, idiotypes of these antibodies induce anti‐idiotypic antibodies that modulate the intensity of the first response, and so on. Adverse effects following SARS‐COV‐2 COVID‐19 vaccines are occasionally similar to the symptoms of COVID‐19 infection. Rare events linked to SARS‐CoV‐2 vaccines also resemble some rarely reported (...) COVID‐19 complications. Safety data from product information by European Medicines Agency suggest that spectra do overlap for four main vaccines. The proposition is that vaccine events and COVID‐19 complications are related to anti‐idiotypic antibodies whose spatial shape can lead to interactions with ACE2 molecules, in individuals with a prolonged Spike protein synthesis. The vaccines target cells by their affinity to the vaccine vector, or to engulf lipid nanoparticles. Anti‐idiotypic antibodies shaped similarly to the Spike protein possibly interact with ACE2 molecules and cause diverse signs and symptoms. (shrink)

This paper deals with the problem of reconstruction of the intergenic interaction graph from the raw data of genetic co-expression coming with new technologies of bio-arrays (DMA-arrays, protein-arrays, etc.). These new imaging devices in general only give information about the asymptotical part (fixed configurations of co-expression or limit cycles of such configurations) of the dynamical evolution of the regulatory networks (genetic and/or proteic) underlying the functioning of living systems. Extracting the casual structure and interaction coefficients of a gene interaction (...) network from the observed configurations is a complex problem. But if all the fixed configurations are supposedly observed and if they are factorizable into two or more subsets of values, then the interaction graph possesses as many connected components as the number of factors and the solution is obtained in polynomial time. This new result allows us for example to partly solve the topology of the genetic regulatory network ruling the flowering in Arabidopsis thaliana. (shrink)

The combined tendency for positive self‐presentation and mimicry or social learning results in the capability of a population of simulated individuals to optimize their cognitive structures. A population of parallel constraint satisfaction networks was created, with globally and locally optimal activation patterns. Individuals started with random activations and interacted to find optimal vectors. Two social network topologies were tested, as well as two modes of interaction; results indicated that the ability to optimize activation vectors depends on the social network (...) configuration and whether individuals are influenced by their best neighbor or are attracted to a neighborhood centroid. Simulated individuals are able to find suitable patterns of belief or attitude with very little internal information processing, using social interaction. (shrink)

Our understanding of how evolution acts on biological networks remains patchy, as is our knowledge of how that action is best identified, modelled and understood. Starting with network structure and the evolution of protein–protein interaction networks, we briefly survey the ways in which network evolution is being addressed in the fields of systems biology, development and ecology. The approaches highlighted demonstrate a movement away from a focus on network topology towards a more integrated view, placing biological properties centre‐stage. (...) We argue that there remains great potential in a closer synergy between evolutionary biology and biological network analysis, although that may require the development of novel approaches and even different analogies for biological networks themselves. (shrink)

The management control system aids administrators in guiding a business toward its organizational plans; as a result, management control is primarily concerned with the execution of the plan and plans. Financial and nonfinancial criteria are used to create management control systems. The financial element focuses on net income, earnings, and other financial metrics. The two components of leadership strategy in this study are cost and differentiation, which highlight the strategy of differentiation in attaining higher quality due to the robust strategy’s (...) attention on a particular area of the company. In this study, we presented a novel method named deep interaction neural network to predict the performance of the manufacturing companies based on their leading competitors using features cost leadership and differentiation strategies. Moreover, the management control system is classified into two financial and nonfinancial factors based on machine learning methods. Based on the results, the presented factors can accurately estimate the company’s performance based on management control criteria with a 93.48% R-square. Moreover, it can be seen that the DT method is presented with higher classification performance values. (shrink)

The impact of multichannel electronic media is assuming a varied effect on the India population as there is a vast cultural and social diversity in the country. While metropolitans had already been influenced by Western culture, the suburbs and rural areas were relatively free from it. This segment of Indian society, ingrained with orthodox values, was not ready for the invasion by Western culture, which is being propa gated by the multichannel electronic media.

The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span (...) from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity. (shrink)