The correlation analysis of the daily Covid-19 new cases data series in Albania

We analyzed herein the new covid-19 daily positive cases recorded in Albania. We observed that the distribution of the daily new cases is non-stationary and usually has a power law behavior in the low incidence zone, and a bell curve for the remaining part of the incidence interval. We qualified this finding as the indicator intensive dynamics and as proof that up now, the heard immunity has not been reached. By parallelizing the preferential attachment mechanisms responsible for a power law distribution in the social graphs elsewhere, we explain the low daily incidence distribution as result of the imprudent gatherings of peoples. Additionally, the bell-shaped distribution observed for the high daily new cases is agued as outcome of the competition between illness advances and restriction measures. The distribution is acceptably smooth, meaning that the management has been accommodated appropriately. This behavior is observed also for two neighbor countries Greece and Italy respectively, but was not observed for Turkey, Serbia, and North Macedonia. Next, we used the multifractal analysis to conclude about the features related with heterogeneity of the data. We have identified the local presence self-organization behavior in some separate time intervals. Formally and empirically we have identified that the full set of the data contain two regimes finalized already, followed by a third one which started in July 2021.

k , l -Anonymity in Wheel-Related Social Graphs Measured on the Base of k -Metric Antidimension

For the study and valuation of social graphs, which affect an extensive range of applications such as community decision-making support and recommender systems, it is highly recommended to sustain the resistance of a social graph G to active attacks. In this regard, a novel privacy measure, called the k , l -anonymity, is used since the last few years on the base of k -metric antidimension of G in which l is the maximum number of attacker nodes defining the k -metric antidimension of G for the smallest positive integer k . The k -metric antidimension of G is the smallest number of attacker nodes less than or equal to l such that other k nodes in G cannot be uniquely identified by the attacker nodes. In this paper, we consider four families of wheel-related social graphs, namely, Jahangir graphs, helm graphs, flower graphs, and sunflower graphs. By determining their k -metric antidimension, we prove that each social graph of these families is the maximum degree metric antidimensional, where the degree of a vertex is the number of vertices linked with that vertex.

Dyads, triads, and tetrads: a multivariate simulation approach to uncovering network motifs in social graphs

Motifs represent local subgraphs that are overrepresented in networks. Several disciplines document multiple instances in which motifs appear in graphs and provide insight into the structure and processes of these networks. In the current paper, we focus on social networks and examine the prevalence of dyad, triad, and symmetric tetrad motifs among 24 networks that represent six types of social interactions: friendship, legislative co-sponsorship, Twitter messages, advice seeking, email communication, and terrorist collusion. Given that the correct control distribution for detecting motifs is a matter of continuous debate, we propose a novel approach that compares the local patterns of observed networks to random graphs simulated from exponential random graph models. Our proposed technique can produce conditional distributions that control for multiple, lower-level structural patterns simultaneously. We find evidence for five motifs using our approach, including the reciprocated dyad, three triads, and one symmetric tetrad. Results highlight the importance of mutuality, hierarchy, and clustering across multiple social interactions, and provide evidence of “structural signatures” within different genres of graph. Similarities also emerge between our findings and those in other disciplines, such as the preponderance of transitive triads.

Twitter Data Import Models

In this paper, the authors describe an algorithm for importing data from the social network Twitter and building weighted social graphs. To import data, the given posts are taken as a basis, users who have had any of the recorded interactions with them are downloaded. Further, the algorithm focuses on the given configuration and uses it to calculate the weights on the edges of the resulting graph. The configuration takes into account the type of user interaction with each other. The authors introduce the concept of (F, L, C, R)-model of information interaction.The authors describe the developed algorithm and implemented software for constructing weighted graphs. The paper shows the application of the algorithm and three models on the example of both a single post and a series of posts.

Assessing the Effectiveness of Social and Political Innovations in the Development of Interaction between the Authorities and the Population during COVID-19: The Implication of Open Innovation

Open innovations combine the interaction of the authorities and the population in regions of Russia. Social and political interaction of Russian network users demonstrates new open forms of political participation, mobilization practices (initiative appeals, petitions), the use of expert systems data, and remote access technologies. The increasing number of initiatives and the growth of online communities involved in the discussion and adjustment of the results of innovation activities require the use of a big data format. The demand for open innovation based on the principles of transparency of social and political interactions is being updated during COVID-19. This study aims to assess the effectiveness of open innovations in social and political interactions during COVID-19. The innovative practices of communication between the population and authorities were studied using DataMining tools based on digital platforms: “Russian Public Initiative”, “Change.org” and “GoogleTrends”. Users’ social graphs represent the visualization in terms of thematic and territorial groupings. The results obtained allow for a conclusion about the dependence of the regional innovation activities on the openness of their communications and their location relative to authoritative and other types of resources. The physical location of the region (center–border region–periphery) and dependence on implementation at the federal, regional or municipal levels are circumstances influencing the effectiveness of social and political innovations.

Graph Neural Networks for Fast Node Ranking Approximation

Graphs arise naturally in numerous situations, including social graphs, transportation graphs, web graphs, protein graphs, etc. One of the important problems in these settings is to identify which nodes are important in the graph and how they affect the graph structure as a whole. Betweenness centrality and closeness centrality are two commonly used node ranking measures to find out influential nodes in the graphs in terms of information spread and connectivity. Both of these are considered as shortest path based measures as the calculations require the assumption that the information flows between the nodes via the shortest paths. However, exact calculations of these centrality measures are computationally expensive and prohibitive, especially for large graphs. Although researchers have proposed approximation methods, they are either less efficient or suboptimal or both. We propose the first graph neural network (GNN) based model to approximate betweenness and closeness centrality. In GNN, each node aggregates features of the nodes in multihop neighborhood. We use this feature aggregation scheme to model paths and learn how many nodes are reachable to a specific node. We demonstrate that our approach significantly outperforms current techniques while taking less amount of time through extensive experiments on a series of synthetic and real-world datasets. A benefit of our approach is that the model is inductive, which means it can be trained on one set of graphs and evaluated on another set of graphs with varying structures. Thus, the model is useful for both static graphs and dynamic graphs. Source code is available at https://github.com/sunilkmaurya/GNN_Ranking

Mobile netware, social graphs, and the reconfiguration of space

Scholars are well-aware that the smartphone is much more than just a mobile telephone. A plethora of applications have been developed to run on smartphones, covering just about every aspect of human life. What is distinctive about the fact that these apps run on smartphones (as opposed to other kinds of devices) is that the smartphone makes them mobile (the apps travel with the user) and locative (the apps know the location of the user). As a result, smartphone applications that take full advantage of these characteristics have the ability to bring users together in real space and real time. The key to the success of such “netware” apps is their generation and retention of social graphs that connect their users both socially and physically. Netware apps like ride hailing that are built around mobility and location have the potential to dramatically restructure economic and social life by reconfiguring their users’ experiences of the physical and temporal world. We use ride hailing as a case study to illustrate how the new social geographies generated by mobile netware apps interact with physical geography to generate a new sense of space that can only be mapped by the companies that “own” our social graphs.