Ending Enclosure by Copying the Commons

In the beginning (of bibliometrics), citation counts of academic research were generated to be used in annual calculations to express a research journal’s impact. Now those same citation counts make up a social graph of scholarly communication that is used to measure the research strengths of authors, the hotness of their papers, the topic prominence of their disciplines, and assess the strength of the institutions where they are employed. More troubling, the publishers of this emerging social graph are in the process of enclosing scholarship by trying to exclude the infrastructure of libraries and other independent, non-profit organizations invested in research. This paper will outline efforts currently being employed by scholarly communication librarians using platforms built by organizations such as Our Research’s UnPaywall and Wikimedia’s Wikidata Project so that the commons of scholarship can remain open. Strategies will be shared so that researchers can adapt their workflows so that they might allow their work to be copied, shared, and be found by readers widely across the commons. Scholars will be asked to make good choices.

A METHOD OF A SOCIAL GRAPH BUILDING IN A GIVEN FIELD OF ACTIVITY USING DATA FROM A SOCIAL NETWORK

В статье описывается способ построения социального графа по данным из социальных сетей в определенной сфере деятельности, опирающийся на активность пользователей в социальных сетях. В статье приводятся данные эксперимента позволяющего оценить точность воспроизведения реальных связей между пользователями в социальном графе. The article provides a method of a social graph construction based on data from social networks in a certain field of activity, as well as on the activity of users in social networks. Furthermore, the paper presents the findings of real data experiment that allows evaluating the accuracy of reproduction of real connections between users in a social graph.

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.

Digital Dystopia

Autocratic regimes, democratic majorities, private platforms, and religious or professional organizations can achieve social control by managing the flow of information about individuals’ behavior. Bundling the agents’ political, organizational, or religious attitudes with information about their prosocial conduct makes them care about behaviors that they otherwise would not. The incorporation of the individuals’ social graph in their social score further promotes soft control but destroys the social fabric. Both bundling and guilt by association are most effective in a society that has weak ties and is politically docile. (JEL D64, D72, D83, D91, K38, Z13)

Migration Pattern As Threshold Parameter In The Propagation of The Covid-19 Epidemic Using An Actor-Based Model for SI-Social Graph

Despite the benefits inherent with social interactions, the case of epidemics cum pandemic outbreaks especially the case of the novel corona virus (covid-19) alongside its set protocols employed to contain the spread therein - has continually left the world puzzled as the disease itself has come to stay. The nature of its rapid propagation on exposure alongside its migration spread pattern of this contagion (with retrospect of other epidemics) on daily basis, has also left experts rethinking the set protocols. Our study involved modelling the covid-19 contagion on a social graph, so as to ascertain if its propagation using migration pattern as a threshold parameter can be minimized via the employment of set protocols. We also employed a design that sought to block or minimize targeted spread of the contagion with the introduction of seedset node(s) using the susceptible-infect framework on a time-varying social graph. Study results showed that migration or mobility pattern has become an imperative factors that must be added when modelling the propagation of contagion or epidemics.

Collective influence maximization for multiple competing products with an awareness-to-influence model

Influence maximization (IM) is a fundamental task in social network analysis. Typically, IM aims at selecting a set of seeds for the network that influences the maximum number of individuals. Motivated by practical applications, in this paper we focus on an IM variant, where the owner of multiple competing products wishes to select seeds for each product so that the collective influence across all products is maximized. To capture the competing diffusion processes, we introduce an Awareness-to-Influence (AtI) model. In the first phase, awareness about each product propagates in the social graph unhindered by other competing products. In the second phase, a user adopts the most preferred product among those encountered in the awareness phase. To compute the seed sets, we propose GCW, a game-theoretic framework that views the various products as agents, which compete for influence in the social graph and selfishly select their individual strategy. We show that AtI exhibits monotonicity and submodularity; importantly, GCW is a monotone utility game. This allows us to develop an efficient best-response algorithm, with quality guarantees on the collective utility. Our experimental results suggest that our methods are effective, efficient, and scale well to large social networks.