Developments in the theory of randomized shortest paths with a comparison of graph node distances

There have lately been several suggestions for parametrized distances on a graph that generalize the shortest path distance and the commute time or resistance distance. The need for developing such distances has risen from the observation that the above-mentioned common distances in many situations fail to take into account the global structure of the graph. In this article, we develop the theory of one family of graph node distances, known as the randomized shortest path dissimilarity, which has its foundation in statistical physics. We show that the randomized shortest path dissimilarity can be easily computed in closed form for all pairs of nodes of a graph. Moreover, we come up with a new definition of a distance measure that we call the free energy distance. The free energy distance can be seen as an upgrade of the randomized shortest path dissimilarity as it defines a metric, in addition to which it satisfies the graph-geodetic property. The derivation and computation of the free energy distance are also straightforward. We then make a comparison between a set of generalized distances that interpolate between the shortest path distance and the commute time, or resistance distance. This comparison focuses on the applicability of the distances in graph node clustering and classification. The comparison, in general, shows that the parametrized distances perform well in the tasks. In particular, we see that the results obtained with the free energy distance are among the best in all the experiments.Comment: 30 pages, 4 figures, 3 table

Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach

Knowledge base completion (KBC) aims to predict missing information in a knowledge base.In this paper, we address the out-of-knowledge-base (OOKB) entity problem in KBC:how to answer queries concerning test entities not observed at training time. Existing embedding-based KBC models assume that all test entities are available at training time, making it unclear how to obtain embeddings for new entities without costly retraining. To solve the OOKB entity problem without retraining, we use graph neural networks (Graph-NNs) to compute the embeddings of OOKB entities, exploiting the limited auxiliary knowledge provided at test time.The experimental results show the effectiveness of our proposed model in the OOKB setting.Additionally, in the standard KBC setting in which OOKB entities are not involved, our model achieves state-of-the-art performance on the WordNet dataset. The code and dataset are available at https://github.com/takuo-h/GNN-for-OOKBComment: This paper has been accepted by IJCAI1

The structure and the evolution of essential patents for standards: Lessons from three IT standards

This paper examines the structure and the evolution of the patents declared as essential for three major technical standards in information technology (MPEG2, DVD and W-CDMA). These standards have many essential patents, which are owned by many firms with different interests. Many patents have been applied even after the standard was set. We analyze three important reasons for why the essential patents are many and increase over time: they cover a number of different technology fields, there exist R&D competition even in a narrowly defined technology field and a firm can expand its patent portfolio by using continuations and other practices based on the priority dates of its earlier filed patent applications in the USA. Around 40% of the essential US patents for MPEG2 and DVD standards have been obtained by using these applications. However, our empirical analysis suggests that a firm with pioneering patents does not obtain more essential patents, using these practices.standard, essential patent, continuations

Ridge Regression, Hubness, and Zero-Shot Learning

This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space. Contrary to the existing approach, which attempts to find a mapping from the example space to the label space, we show that mapping labels into the example space is desirable to suppress the emergence of hubs in the subsequent nearest neighbor search step. Assuming a simple data model, we prove that the proposed approach indeed reduces hubness. This was verified empirically on the tasks of bilingual lexicon extraction and image labeling: hubness was reduced with both of these tasks and the accuracy was improved accordingly.Comment: To be presented at ECML/PKDD 201

Globalizing financial valuation: International property consultants in São Paulo

International property consultants (IPCs) have become key intermediaries in the globalization of property markets by providing a range of services that generate transparency and comparability in land and property-based investments. While their role in generating standardized information on local markets is well known, what is less is known is how IPCs help turn property into an income-yielding asset in less developed economies. This article investigates the contested diffusion of financialized valuation approaches in São Paulo’s local property market. Through a qualitative inquiry into large IPCs and their main clients in the city, we show that IPCs have promoted valuation approaches that are tailored to the needs of financial market investors, thus affecting key investment decisions taken by diverse actors. Though these financialized techniques have at times clashed with more traditional views of property ownership prevalent in the country, we show that most often they co-exist with long-established valuation techniques that reflect the social and economic circumstances of Brazil’s economy. The socially contingent nature of property valuation raises theoretical issues concerning the complexity of attributing value to fixed capital, as well as several policy issues