Node aggregation for enhancing PageRank

In this paper, we study the problem of node aggregation under different perspectives for increasing PageRank of some nodes of interest. PageRank is one of the parameters used by the search engine Google to determine the relevance of a web page. We focus our attention to the problem of nding the best nodes in the network from an aggregation viewpoint, i.e., what are the best nodes to merge with for the given nodes. This problem is studied from global and local perspectives. Approximations are proposed to reduce the computation burden and to overcome the limitations resulting from the lack of centralized information. Several examples are presented to illustrate the different approaches that we proposeFP7-ICT Project DYMASOS under Grant 611281Ministerio de Economía y Competitividad COOPERA Project Grant DPI2013-46912-C2-1-RMinisterio de Educación José Castillejo Grant (CAS14/00277)Grant-in-Aid for Scientific Research Grant 15H04020 and Fellowship PE1604

Algorithm Instance Games

This paper introduces algorithm instance games (AIGs) as a conceptual classification applying to games in which outcomes are resolved from joint strategies algorithmically. For such games, a fundamental question asks: How do the details of the algorithm's description influence agents' strategic behavior? We analyze two versions of an AIG based on the set-cover optimization problem. In these games, joint strategies correspond to instances of the set-cover problem, with each subset (of a given universe of elements) representing the strategy of a single agent. Outcomes are covers computed from the joint strategies by a set-cover algorithm. In one variant of this game, outcomes are computed by a deterministic greedy algorithm, and the other variant utilizes a non-deterministic form of the greedy algorithm. We characterize Nash equilibrium strategies for both versions of the game, finding that agents' strategies can vary considerably between the two settings. In particular, we find that the version of the game based on the deterministic algorithm only admits Nash equilibrium in which agents choose strategies (i.e., subsets) containing at most one element, with no two agents picking the same element. On the other hand, in the version of the game based on the non-deterministic algorithm, Nash equilibrium strategies can include agents with zero, one, or every element, and the same element can appear in the strategies of multiple agents.Comment: 14 page

First Women, Second Sex: Gender Bias in Wikipedia

Contributing to history has never been as easy as it is today. Anyone with access to the Web is able to play a part on Wikipedia, an open and free encyclopedia. Wikipedia, available in many languages, is one of the most visited websites in the world and arguably one of the primary sources of knowledge on the Web. However, not everyone is contributing to Wikipedia from a diversity point of view; several groups are severely underrepresented. One of those groups is women, who make up approximately 16% of the current contributor community, meaning that most of the content is written by men. In addition, although there are specific guidelines of verifiability, notability, and neutral point of view that must be adhered by Wikipedia content, these guidelines are supervised and enforced by men. In this paper, we propose that gender bias is not about participation and representation only, but also about characterization of women. We approach the analysis of gender bias by defining a methodology for comparing the characterizations of men and women in biographies in three aspects: meta-data, language, and network structure. Our results show that, indeed, there are differences in characterization and structure. Some of these differences are reflected from the off-line world documented by Wikipedia, but other differences can be attributed to gender bias in Wikipedia content. We contextualize these differences in feminist theory and discuss their implications for Wikipedia policy.Comment: 10 pages, ACM style. Author's version of a paper to be presented at ACM Hypertext 201