arXiv subjects

Bowen Yan

Publications and source records attributed to Bowen Yan.

27 records · Page 2Linked to original sources

arXiv2017

Mapping Patent Classifications: Portfolio and Statistical Analysis, and the Comparison of Strengths and Weaknesses

The Cooperative Patent Classifications (CPC) jointly developed by the European and US Patent Offices provide a new basis for mapping and portfolio analysis. This update provides an occasion for rethinking the parameter choices. The new maps are significantly different from previous ones, although this may not always be obvious on visual inspection. Since these maps are statistical constructs based on index terms, their quality--as different from utility--can only be controlled discursively. We provide nested maps online and a routine for portfolio overlays and further statistical analysis. We add a new tool for "difference maps" which is illustrated by comparing the portfolios of patents granted to Novartis and MSD in 2016.

cs.DL↗

arXiv2016

Filtering Patent Maps for Visualization of Diversification Paths of Inventors and Organizations

In the information science literature, recent studies have used patent databases and patent classification information to construct network maps of patent technology classes. In such a patent technology map, almost all pairs of technology classes are connected, whereas most of the connections between them are extremely weak. This observation suggests the possibility of filtering the patent network map by removing weak links. However, removing links may reduce the explanatory power of the network on inventor or organization diversification. The network links may explain the patent portfolio diversification paths of inventors and inventing organizations. We measure the diversification explanatory power of the patent network map, and present a method to objectively choose an optimal trade-off between explanatory power and removing weak links. We show that this method can remove a degree of arbitrariness compared with previous filtering methods based on arbitrary thresholds, and also identify previous filtering methods that created filters outside the optimal trade-off. The filtered map aims to aid in network visualization analyses of the technological diversification of inventors, organizations and other innovation agents, and potential foresight analysis. Such applications to a prolific inventor (Leonard Forbes) and company (Google) are demonstrated.

cs.SI↗

arXiv2016

Mapping Technology Space by Normalizing Patent Networks

Technology is a complex system, with technologies relating to each other in a space that can be mapped as a network. The technology network's structure can reveal properties of technologies and of human behavior, if it can be mapped accurately. Technology networks have been made from patent data, using several measures of proximity. These measures, however, are influenced by factors of the patenting system that do not reflect technologies or their proximity. We introduce a method to precisely normalize out multiple impinging factors in patent data and extract the true signal of technological proximity, by comparing the empirical proximity measures with what they would be in random situations that remove the impinging factors. With this method, we created technology networks, using data from 3.9 million patents. After normalization, different measures of proximity became more correlated with each other, approaching a single dimension of technological proximity. The normalized technology networks were sparse, with few pairs of technology domains being significantly related. The normalized network corresponded with human behavior: we analyzed the patenting histories of 2.8 million inventors and found they were more likely to invent in two different technology domains if the pair was closely related in the technology network. We also analyzed 250 thousand firms' patents and found that, in contrast, firms' inventive activities were only modestly associated with the technology network; firms' portfolios combined pairs of technology domains about twice as often as inventors. These results suggest that controlling for impinging factors provides meaningful measures of technological proximity for patent-based mapping of the technology space, and that this map can be used to aid in technology innovation planning and management.

physics.soc-ph↗

arXiv2015

Measuring Technological Distance for Patent Mapping

Recent works in the information science literature have presented cases of using patent databases and patent classification information to construct network maps of technology fields, which aim to aid in competitive intelligence analysis and innovation decision making. Constructing such a patent network requires a proper measure of the distance between different classes of patents in the patent classification systems. Despite the existence of various distance measures in the literature, it is unclear how to consistently assess and compare them, and which ones to select for constructing patent technology network maps. This ambiguity has limited the development and applications of such technology maps. Herein, we propose to compare alternative distance measures and identify the superior ones by analyzing the differences and similarities in the structural properties of resulting patent network maps. Using United States patent data from 1976 to 2006 and International Patent Classification system, we compare 12 representative distance measures, which quantify inter-field knowledge base proximity, field-crossing diversification likelihood or frequency of innovation agents, and co-occurrences of patent classes in the same patents. Our comparative analyses suggest the patent technology network maps based on normalized co-reference and inventor diversification likelihood measures are the best representatives.

cs.SI↗

arXiv2013

Identifying Communities and Key Vertices by Reconstructing Networks from Samples

Sampling techniques such as Respondent-Driven Sampling (RDS) are widely used in epidemiology to sample "hidden" populations, such that properties of the network can be deduced from the sample. We consider how similar techniques can be designed that allow the discovery of the structure, especially the community structure, of networks. Our method involves collecting samples of a network by random walks and reconstructing the network by probabilistically coalescing vertices, using vertex attributes to determine the probabilities. Even though our method can only approximately reconstruct a part of the original network, it can recover its community structure relatively well. Moreover, it can find the key vertices which, when immunized, can effectively reduce the spread of an infection through the original network.

cs.SI↗

arXiv2012

Finding missing edges in networks based on their community structure

Many edge prediction methods have been proposed, based on various local or global properties of the structure of an incomplete network. Community structure is another significant feature of networks: Vertices in a community are more densely connected than average. It is often true that vertices in the same community have "similar" properties, which suggests that missing edges are more likely to be found within communities than elsewhere. We use this insight to propose a strategy for edge prediction that combines existing edge prediction methods with community detection. We show that this method gives better prediction accuracy than existing edge prediction methods alone.

cs.IR↗

arXiv2012

Detecting Communities in Networks by Merging Cliques

Many algorithms have been proposed for detecting disjoint communities (relatively densely connected subgraphs) in networks. One popular technique is to optimize modularity, a measure of the quality of a partition in terms of the number of intracommunity and intercommunity edges. Greedy approximate algorithms for maximizing modularity can be very fast and effective. We propose a new algorithm that starts by detecting disjoint cliques and then merges these to optimize modularity. We show that this performs better than other similar algorithms in terms of both modularity and execution speed.

physics.soc-ph↗

arXiv2012

Detecting community structure in networks using edge prediction methods

Community detection and edge prediction are both forms of link mining: they are concerned with discovering the relations between vertices in networks. Some of the vertex similarity measures used in edge prediction are closely related to the concept of community structure. We use this insight to propose a novel method for improving existing community detection algorithms by using a simple vertex similarity measure. We show that this new strategy can be more effective in detecting communities than the basic community detection algorithms.

physics.soc-ph↗

arXiv2011

Finding missing edges and communities in incomplete networks

Many algorithms have been proposed for predicting missing edges in networks, but they do not usually take account of which edges are missing. We focus on networks which have missing edges of the form that is likely to occur in real networks, and compare algorithms that find these missing edges. We also investigate the effect of this kind of missing data on community detection algorithms.

cs.SI↗