Detect Overlapping Community Based on the Combination of Local Expansion and Label Propagation

It is a common phenomenon in real life that individuals have diverse member relationships in different social clusters, which is called overlap in the science of network. Detecting overlapping components of the community structure in a network has extensive value in real-life applications. The mainstream algorithms for community detection generally focus on optimization of a global or local static metric. These algorithms are often not good when the community characteristics are diverse. In addition, there is a lot of randomness in the process of the algorithm. We proposed a algorithm combining local expansion and label propagation. In the stage of local expansion, the seed is determined by the node pair with the largest closeness, and the rule of expansion also depends on closeness. Local expansion is just to obtain the center of expected communities instead of final communities, and these immature communities leave only dense regions after pruning according to certain rules. Taking the dense regions as the source makes the label propagation reach stability rapidly in the early propagation so that the final communities are detected more accurately. The experiments in synthetic and real-world networks proved that our algorithm is more effective not only on the whole, but also at the level of the node. In addition, it is stable in the face of different network structures and can maintain high accuracy.

A new theory of social roles in networks

Theories of social roles neglect social clusters. However, accounting for clusters is essential because individuals in social networks (e.g., social media) cannot oversee the whole network and have to restrict their interactions to local substructures. Roles that do not account for this cluster formation may lead to misinterpretations of the network’s dynamics and functions. This article proposes a theory of social roles in large social networks. We group roles detected in previous empirical studies into meta-roles and embed them along two dimensions, strategicness describing whether the person works towards a particular goal or not, and the type of strategy (selfish or group-oriented). We extend this framework by adding a cluster dimension describing to what extent a person’s interactions are embedded locally or globally in the network. We argue that empirical role analyses would benefit from our theory by systematically accounting for complex structures specific to the network perspective, generalising empirical findings beyond individual case studies, and understanding human interactions better.

On the Use of Neural Networks to Solve the Social Clustering Problem

The problem of social clustering being studied in the paper is one of the main subtasks; its solution is an integral part of analysis and prognosis of socio-economic processes. Analysis and systematization of knowledge in the field of applying neural network modelling to regional system social clustering problem solving are implemented. It was demonstrated that today, the main factor of economic growth is human capital, which is composed of quantitative and qualitative features. The main quantitative element is population replacement which has a bearing on human capital development sustainability. Qualitative component has several aspects in it: healthcare, culture, education and science are among them. To estimate human capital structure, the population is divided into social clusters by these aspects. It was also shown that since social cluster is an attribute of sociogenesis, processes of social clustering themselves are the result of people social interactions. Social cluster is a specific state of social entity which includes description of not only entity’s objects, but the processes which led to its structural development and interactions with social environment. As part of the study, a conclusion was made that neural networks enable one to apply cluster analysis to the society. Neural networks prove notable capabilities to solve poorly formalized tasks; they are resistant to frequent environmental changes and effective to use when working with a large amount of incomplete or contradictory information. While studying the issue, it was observed that structural and statistical features of social clusters reflect aggregation of their elements. The structure of a social cluster is a characteristic which represents a conjunction of stable connections which provide its unity. Under different external and internal changes, the main properties of social clusters are preserved. The grading of social demographic elements by health condition and cultural and educational level is set, in accordance with which collecting a statistical data to solve the clustering problem is implemented.

Ultrametric model for covid-19 dynamics: an attempt to explain slow approaching herd immunity in Sweden

We present a mathematical model of infection dynamics that might explain slower approaching the herd immunity during the covid-19 epidemy in Sweden than it was predicted by a variety of other models; see graphs Fig. 2. The new model takes into account the hierarchic structure of social clusters in the human society. We apply the well developed theory of random walk on the energy landscapes represented mathematically with ultrametric spaces. This theory was created for applications to spin glasses and protein dynamics. To move from one social cluster (valley) to another, the virus (its carrier) should cross a social barrier between them. The magnitude of a barrier depends on the number of social hierarchy’s levels composing this barrier. As the most appropriate for the recent situation in Sweden, we consider linearly increasing (with respect to hierarchy’s levels) barriers. This structure of barriers matches with a rather soft regulations imposed in Sweden in March 2020. In this model, the infection spreads rather easily inside a social cluster (say working collective), but jumps to other clusters are constrained by social barriers. This model’s feature matches with the real situation during the covid-19 epidemy, with its cluster spreading structure. Clusters need not be determined solely geographically, they are based on a number of hierarchically ordered social coordinates. The model differs crucially from the standard mathematical models of spread of disease, such as the SIR-model. In particular, our model describes such a specialty of spread of covid-19 virus as the presence of “super-spreaders” who by performing a kind of random walk on a hierarchic landscape of social clusters spreads infection. In future, this model will be completed by adding the SIR-type counterpart. But, the latter is not a specialty of covid-19 spreading.

Ultrametric Model for COVID-19 Dynamics: An Attempt to Explain Slow Approaching Herd Immunity in Sweden

We present a model of infection dynamics that might explain slower approaching the herd immunity during the covid-19 epidemy in Sweden than it was predicted by a variety of other models; see graphs Fig. \ref{GROWTH2}. The new model takes into account the hierarchic structure of social clusters in the human society. We apply the well developed theory of random walk on the energy landscapes represented mathematically with ultrametric spaces. This theory was created for applications to spin glasses and protein dynamics. To move from one social cluster (valley) to another, the virus should cross a social barrier between them. The magnitude of a barrier depends on the number of social hierarchy's levels composing this barrier. As the most appropriate for the recent situation in Sweden, we consider linearly increasing (with respect to hierarchy's levels) barriers. This structure of barriers matches with a rather soft regulations imposed in Sweden in March 2020. In this model, the infection spreads rather easily inside a social cluster (say working collective), but jumps to other clusters are constrained by social barriers. This model's feature matches with the real situation during the covid-19 epidemy, with its cluster spreading structure. Clusters need not be determined solely geographically, they are based on a number of hierarchically ordered social coordinates. The model differs crucially from the standard models of spread of disease, such as the SIR-model. Our model describes such a specialty of spread of covid-19 virus as the presence of ``super-infectors'' who by performing a kind of random walk on a hierarchic landscape of social clusters spreads infection. In future, this model will be completed by adding the SIR-type counterpart. But, the latter is not a specialty of covid-19 spreading.

Modeling and forecasting of the dynamics of “quality of life” of the population of Krasnodar Krai

The goal of this research consists in the analysis of trends in a compositely structured index of the “quality of life”, and analysis of sensitivity of a complex indicator by separate factors and cross-section of population of Krasnodar Krai. Based on the acquired results, the author builds a mathematical economic model for the analysis and forecasting of changes in the quality of life of the population of Krasnodar Krai in the context of diverse clusters of municipal formations, as well as compares different functions of approximation. The subject of this research is the socioeconomic interaction within the regional system of Krasnodar Krai. The article carries out a retrospective analysis is conducted on the most important indexes characterizing the subject field, correlation-regression analysis of variables, and sensitivity analysis based on the coefficients of elasticity of private factors. The author’s main contribution into the research of this topic lies in building the regression models of the quality of life index of the population of Krasnodar Krai in the context of diverse social clusters, as wll as in determination of the degree of impact of separate social, demographic or economic factors upon complex indicator. Such models allow forecasting and carrying out experimental modeling in this area.

Information brokers and the making of the Baring crisis, 1857–1890

This article offers a new interpretation of the Baring crisis, the most dramatic financial collapse of the nineteenth century, by focusing on how information brokerage allowed Barings to abandon its risk-averse practices in the mid 1880s. I argue that the mediators who bridged structural holes (gaps between social clusters) shaped actors’ access to information as well as their expectations regarding its quality. Information brokers who enjoyedphilosties with at least one of the parties connected by the bridging relationships could promote collaborative arrangements more likely to survive an environment of heightened uncertainty. The performance of such brokers in the 1880s enabled cooperation between Baring Brothers & Co. and the Banque de Paris et des Pays Bas and supported the London house's growing association with the Anglo-Argentine firm of S. B. Hale & Co. in the second half of the 1880s. Cooperation gave Barings an illusion of security amid the costs of increasing competition and supported the house's growing engagement in South American affairs. Nevertheless, the strategy proved ineffective at barring the entry of new players. By the late 1880s, ties produced by brokerage connected Barings to the house's former competitors, producing a cohesive social cluster. Barings thereafter had access to redundant information, which hindered the house's ability to assess risk.

Casting off the Shackles: Re-narrativizing Discrepant Terrains of Memories with Lived Experiences in A. B. Yehoshua’s Facing the Forests

The ever-increasing human migration with fast-eroding bonds often leads to development of social clusters which tend to produce stereotypes about other groups of people. One among the many factors responsible for such ghettoization is not ignorance but a version of knowledge that does not take us beyond ourselves. Speaking about the role of writer as “the conscience of the nation,” S. Yizhar, an Israeli writer and politician, says that a writer is a mutation. Literature is one such powerful tool to transcend cultural boundaries which takes us beyond ourselves and reaches to the other side. Holding the case of Israeli–Palestinian contestation as one such manifestation of cultural ghettoization, the article argues for the need to render open the mental borders to move beyond prejudices. Taking A. B. Yehoshua’s Facing the Forests as a point of departure, it highlights the potential of dialog as a powerful antidote to social violence between the two communities of Arabs and Israelis and lays bare its importance in the direction of peaceful coexistence. The article is divided into three sections; the first section introduces the cultural location of Yehoshua and his narrative set in the Israeli landscape of the 1960s. The second section explores the use of symbolism in the narrative to cull out the dormant meanings and analyzes the multiple layers of the text. The third and last section projects the idea of re-narrativization as an important tool of re-inscribing history with alternative versions. It draws strength from those Arab–Jewish encounters that are not necessarily troubling. The conclusion sums up the essential findings of the study, elucidating the role of literature and intellectuals in difficult times.