Mining Missing Hyperlinks from Human Navigation Traces: A Case Study of Wikipedia

Hyperlinks are an essential feature of the World Wide Web. They are especially important for online encyclopedias such as Wikipedia: an article can often only be understood in the context of related articles, and hyperlinks make it easy to explore this context. But important links are often missing, and several methods have been proposed to alleviate this problem by learning a linking model based on the structure of the existing links. Here we propose a novel approach to identifying missing links in Wikipedia. We build on the fact that the ultimate purpose of Wikipedia links is to aid navigation. Rather than merely suggesting new links that are in tune with the structure of existing links, our method finds missing links that would immediately enhance Wikipedia's navigability. We leverage data sets of navigation paths collected through a Wikipedia-based human-computation game in which users must find a short path from a start to a target article by only clicking links encountered along the way. We harness human navigational traces to identify a set of candidates for missing links and then rank these candidates. Experiments show that our procedure identifies missing links of high quality

Missing Links

There are no doubts that the African telecommunication sector has grown and made significant strides the last three years. The level of progress is not a fluke. However, one of the greatest problems facing affordable telecommunication access in many parts of Africa is monopoly of access, links and inter-connectivity between operators. In many countries, this monopoly is controlled by incumbents, legacies of state owned telecommunication companies failing to realise when their job is done and when relinquishing their hold on national structures is more nationally productive. Often the links in question have been paid for with tax payers’ money before such companies are privatised or sold. This problem is significant across the African continent and has kept communication access in the continent very expensive

Uncovering missing links with cold ends

To evaluate the performance of prediction of missing links, the known data are randomly divided into two parts, the training set and the probe set. We argue that this straightforward and standard method may lead to terrible bias, since in real biological and information networks, missing links are more likely to be links connecting low-degree nodes. We therefore study how to uncover missing links with low-degree nodes, namely links in the probe set are of lower degree products than a random sampling. Experimental analysis on ten local similarity indices and four disparate real networks reveals a surprising result that the Leicht-Holme-Newman index [E. A. Leicht, P. Holme, and M. E. J. Newman, Phys. Rev. E 73, 026120 (2006)] performs the best, although it was known to be one of the worst indices if the probe set is a random sampling of all links. We further propose an parameter-dependent index, which considerably improves the prediction accuracy. Finally, we show the relevance of the proposed index on three real sampling methods.Comment: 16 pages, 5 figures, 6 table

Missing Links in Multiple Trade Networks

In this paper we develop a network model of international trade which is able to replicate the concentrated and sparse nature of trade data. Our model extends the preferential attachment (PA) growth model to the case of multiple networks. Countries trade a variety of goods of different complexity. Every country progressively evolves from trading less sophisticated to high-tech goods. The probability to capture more trade opportunities at a given level of complexity and to start trading more complex goods are both proportional to the number of existing trade links. We provide a set of theoretical predictions and simulative results. A calibration exercise shows that our model replicates the same concentration level of world trade as well as the sparsity pattern of the trade matrix. Moreover, we find a lower bound for the share of genuine missing trade links. We also discuss a set of numerical solutions to deal with large multiple networks

Entropy-based approach to missing-links prediction

Link-prediction is an active research field within network theory, aiming at uncovering missing connections or predicting the emergence of future relationships from the observed network structure. This paper represents our contribution to the stream of research concerning missing links prediction. Here, we propose an entropy-based method to predict a given percentage of missing links, by identifying them with the most probable non-observed ones. The probability coefficients are computed by solving opportunely defined null-models over the accessible network structure. Upon comparing our likelihood-based, local method with the most popular algorithms over a set of economic, financial and food networks, we find ours to perform best, as pointed out by a number of statistical indicators (e.g. the precision, the area under the ROC curve, etc.). Moreover, the entropy-based formalism adopted in the present paper allows us to straightforwardly extend the link-prediction exercise to directed networks as well, thus overcoming one of the main limitations of current algorithms. The higher accuracy achievable by employing these methods - together with their larger flexibility - makes them strong competitors of available link-prediction algorithms

Missing Links: Referrer Behavior and Job Segregation

The importance of networks in labor markets is well-known, and their job segregating effects in organizations taken as granted. Conventional wisdom attributes this segregation to the homophilous nature of contact networks, and leaves little role for organizational influences. But employee referrals are necessarily initiated within a firm by employee referrers subject to organizational policies. We build theory regarding the role of referrers in the segregating effects of network recruitment. Using mathematical and computational models, we investigate how empirically-documented referrer behaviors affect job segregation. We show that referrer behaviors can segregate jobs beyond the effects of homophilous network recruitment. Further, and contrary to past understandings, we show that referrer behaviors can also mitigate most if not all of the segregating effects of network recruitment. Although largely neglected in previous labor market network scholarship, referrers are the missing links revealing opportunities for organizations to influence the effects of network recruitment