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AbstractViral marketing campaigns target primarily those individuals who are central in social networks and hence have social influence. Marketing events, however, may attract diverse audience. Despite the importance of event marketing, the influence of heterogeneous target groups is not well understood yet. In this paper, we define the Audience Selection (AS) problem in which different sets of agents need to be evaluated and compared based on their social influence. A typical application of Audience selection is choosing locations for a series of marketing events. The Audience selection problem is different from the well-known Influence Maximization (IM) problem in two aspects. Firstly, it deals with sets rather than nodes. Secondly, the sets are diverse, composed by a mixture of influential and ordinary agents. Thus, Audience selection needs to assess the contribution of ordinary agents too, while IM only aims to find top spreaders. We provide a systemic test for ranking influence measures in the Audience Selection problem based on node sampling and on a novel statistical method, the Sum of Ranking Differences. Using a Linear Threshold diffusion model on two online social networks, we evaluate eight network measures of social influence. We demonstrate that the statistical assessment of these influence measures is remarkably different in the Audience Selection problem, when low-ranked individuals are present, from the IM problem, when we focus on the algorithm's top choices exclusively.

Sum of Ranking Differences is an innovative statistical method that ranks competing solutions based on a reference point. The latter might arise naturally, or can be aggregated from the data. We provide two case studies to feature both possibilities. Apportionment and districting are two critical issues that emerge in relation to democratic elections. Theoreticians invented clever heuristics to measure malapportionment and the compactness of the shape of the constituencies, yet, there is no unique best method in either cases. Using data from Norway and the US we rank the standard methods both for the apportionment and for the districting problem. In case of apportionment, we find that all the classical methods perform reasonably well, with subtle but significant differences. By a small margin the Leximin method emerges as a winner, but - somewhat unexpectedly - the nonregular Imperiali method ties for first place. In districting, the Lee-Sallee index and a novel parametric method the so-called Mo ent Invariant performs the best, although the latter is sensitive to the function's chosen parameter.

AbstractAlthough cross-validation (CV) is a standard technique in machine learning and data science, its efficacy remains largely unexplored in ranking environments. When evaluating the significance of differences, cross-validation is typically coupled with statistical testing, such as the Dietterich, Alpaydin, or Wilcoxon test. In this paper, we evaluate the power and false positive error rate of the Dietterich, Alpaydin, and Wilcoxon statistical tests combined with cross-validation each operating with folds ranging from 5 to 10, resulting in a total of 18 variants. Our testing setup utilizes a ranking framework, similar to the Sum of Ranking Differences (SRD) statistical procedure: we assume the existence of a reference ranking, and distances are measured in $$L_1$$

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-norm. We test the methods under artificial scenarios as well as on real data borrowed from sports and chemistry. The choice of the optimal CV test method depends on preferences related to the minimization of errors in type I and II cases, the size of the input, and anticipated patterns in the data. Among the investigated input sizes, the Wilcoxon method with eight folds proved to be the most effective, although its performance in type I situations is subpar. While the Dietterich and Alpaydin methods excel in type I situations, they perform poorly in type II scenarios. The inadequate performances of these tests raises questions about their efficacy outside of ranking environments too.

We aim to identify the most influential members of the Agricultural Committee of the European Parliament (COMAGRI). Unlike previous studies that were based on case studies or interviews with stakeholders, we analyse the voting power of MEPs using a spatial Banzhaf power index. We identify critical members: members whose votes are necessary to form winning coalitions. We found that rapporteurs, EP group coordinators and MEPs from countries with high relative Committee representations, such as Ireland, Poland or Romania are powerful actors. Italy emerges as the most influential member state, while France seems surprisingly weak.