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The popularity of the Web has allowed individuals to communicate and interact with each other on a global scale: people connect both to close friends and acquaintances, creating ties that can bridge otherwise separated groups of people. Recent evidence suggests that spatial distance is still affecting social links established on online platforms, with online ties preferentially connecting closer people. In this work we study the relationships between interaction strength, spatial distance and structural position of ties between members of a large-scale online social networking platform, Tuenti. We discover that ties in highly connected social groups tend to span shorter distances than connections bridging together otherwise separated portions of the network. We also find that such bridging connections have lower social interaction levels than ties within the inner core of the network and ties connecting to its periphery. Our results suggest that spatial constraints on online social networks are intimately connected to structural network properties, with important consequences for information diffusion.

Personalized (mobile) devices are radically changing information access tools, with rich context allowing for far more powerful, personalized search. Rather than retrieving a “document” on the topic of a “query,” the rich contextual information allows for tailored search and recommendation, and solve user’s complex tasks by taking into account complex constraints, exploring options, and combining individual answers into a coherent whole. This paper reports on a RuSSIR 2015 course covering the challenges of contextual search and recommendation, with a concrete focus on the venue recommendation task as run as part of TREC 2012–2015. It consisted of both lectures and hands-on “hackathon” sessions with data derived from the TREC task.

Comunicació presentada a: The Tenth International AAAI Conference on web and social media (ICWSM 2016), celebrat del 18 al 20 de maig de 2016 a Colònia, Alemanya. Social media has become a key mechanism for the organization of grassroots movements. In the 2015 Barcelona City Council election, Barcelona en Com´u, an emerging grassroots party, was the most voted one. This candidacy was devised by activists involved in the Spanish 15M movement in order to turn citizen outrage into political change. On the one hand, the 15M movement is based on a decentralized structure. On the other hand, political science literature postulates that parties historically develop oligarchical leadership structures. This tension motivates us to examine whether Barcelona en Com´u preserved a decentralized structure or adopted a conventional centralized organization. In this article we propose a computational framework to analyze the Twitter networks of the parties that ran for this election by measuring their hierarchical structure, small-world phenomenon and coreness. The results of our assessment show that in Barcelona en Com´u two well-defined groups co-exist: a cluster dominated by the party leader and the collective accounts, and another cluster formed by the movement activists. While the former group is highly centralized like traditional parties, the latter one stands out for its decentralized, cohesive and resilient structure. This work is supported by the EU project D-CENT (FP7 CAPS 610349). We would like to thank DatAnalysis15M Research Network and the #GlobalRevExp Forum for their valuable discussions and suggestions that helped to improve this study. Yana Volkovich is supported by the People Programme (Marie Curie Actions, from the FP7/2007-2013) under grant agreement no.600388 managed by REA and ACC1O´ .

This paper presents an approach for automatically retrieving family relationships from a real-world collection of Dutch historical notary acts. We aim to retrieve relationships like husband - wife, parent - child, widow of, etc. Our approach includes person names extraction, reference disambiguation, candidate generation and family relationship prediction. Since we have a limited amount of training data, we evaluate different feature configurations based on the n-gram analysis. The best results were obtained by using a combination of bi-grams and tri-grams of words together with the distance in words between two names. We evaluate our results for each type of the relationships in terms of precision, recall and \(f-score\).

The dependencies between power law parameters such as in-degree and PageRank, can be characterized by the so-called angular measure, a notion used in extreme value theory to describe the dependency between very large values of coordinates of a random vector. Basing on an analytical stochastic model, we argue that the angular measure for in-degree and personalized PageRank is concentrated in two points. This corresponds to the two main factors for high ranking: large in-degree and a high rank of one of the ancestors. Furthermore, we can formally establish the relative importance of these two factors.

We analyze dependencies in complex networks characterized by power laws (Web sample, Wikipedia sample and a preferential attachment graph) using statistical techniques from the extreme value theory and the theory of multivariate regular variation. To the best of our knowledge, this is the first attempt to apply this well developed methodology to comprehensive graph data. The new insights this yields are striking: the three above-mentioned data sets are shown to have a totally different dependence structure between graph parameters, such as in-degree and PageRank. Based on the proposed approach, we suggest a new measure for rank correlations. Unlike most known methods, this measure is especially sensitive to rank permutations for top-ranked nodes. Using the new correlation measure, we demonstrate that the PageRank ranking is not sensitive to moderate changes in the damping factor.

We analyze dependencies in power law graph data (Web sample, Wikipedia sample and a preferential attachment graph) using statistical inference for multivariate regular variation. To the best of our knowledge, this is the first attempt to apply the well developed theory of regular variation to graph data. The new insights this yields are striking: the three above-mentioned data sets are shown to have a totally different dependence structure between different graph parameters, such as in-degree and PageRank. Based on the proposed methodology, we suggest a new measure for rank correlations. Unlike most known methods, this measure is especially sensitive to rank permutations for topranked nodes. Using this method, we demonstrate that the PageRank ranking is not sensitive to moderate changes in the damping factor.

We study the relation between PageRank and other parameters of information networks such as in-degree, out-degree, and the fraction of dangling nodes. We model this relation through a stochastic equation inspired by the original definition of PageRank. Further, we use the theory of regular variation to prove that PageRank and in-degree follow power laws with the same exponent. The difference between these two power laws is in a multiple coefficient, which depends mainly on the fraction of dangling nodes, average in-degree, the power law exponent, and damping factor. The out-degree distribution has a minor effect, which we explicitly quantify. Our theoretical predictions show a good agreement with experimental data on three different samples of the Web.

The PageRank is a popularity measure designed by Google to rank Web pages. Experiments confirm that the PageRank obeys a 'power law' with the same exponent as the In-Degree. This paper presents a novel mathematical model that explains this phenomenon. The relation between the PageRank and In-Degree is modelled through a stochastic equation, which is inspired by the original definition of the PageRank, and is analogous to the well-known distributional identity for the busy period in the M/G/l queue. Further, we employ the theory of regular variation and Tauberian theorems to analytically prove that the tail behavior of the PageRank and the In-Degree differ only by a multiplicative factor, for which we derive a closed-form expression. Our analytical results are in good agreement with experimental data.

In this paper, we describe the motivations for the organization of the SISoM-workshop on Social Innovation and Social Media, to be held in the context of the ICWSM 2011. We argue that social innovation and social media, two trends, that have started independently, are bound to increasingly overlap. Social innovators, as well as the growing number of public administrations fostering social innovation, regard social media as a potentially powerful communication and coordination tool. At the same time, it is argued that they will require functionalities and implement practices which differ from those prevalent in the most popular general purpose social media platforms. A theoretical framework and an initial set of research questions are proposed in order to address those matters, which are partially covered by the papers accepted as contributions to the workshop.