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Multi-Label Patent Categorization with Non-Local Attention-Based Graph Convolutional Network
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In this work, we propose a label attention model based on graph convolutional network. ...
On a large CIRCA patent database, we evaluate the performance of our model and as many as seven competitive baselines. ...
AnnexML (Tagami 2017 ) presents a graph embedding method to cope with several limitations of SLEEC. ...
doi:10.1609/aaai.v34i05.6435
fatcat:z25s27ojf5f7vnbh7bgmjpdziu
Subset Node Representation Learning over Large Dynamic Graphs
[article]
2021
arXiv
pre-print
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Dynamic graph representation learning is a task to learn node embeddings over dynamic networks, and has many important applications, including knowledge graphs, citation networks to social networks. ...
Based on recent advances in local node embedding and a novel computation of dynamic personalized PageRank vector (PPV), DynamicPPE has two key ingredients: 1) the per-PPV complexity is 𝒪(m d̅ / ϵ) where ...
Patent (US Patent Graph) The citation network of US patent[14]
contains 2,738,011 nodes with 13,960,811 citations range from year
1963 to 1999. ...
arXiv:2106.01570v1
fatcat:iegarl2fsbhutb4sesspfab6he
Guest Editorial: Tech Mining for Engineering Management: An Introduction
2021
IEEE transactions on engineering management
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Competitors' Innovation Activities: Analyzing the Competitive Patent Landscape Based on Semantic Anchor Points, in which the authors specifically focused on the activities of patent assignees and proposed ...
CTI-Oriented Patent Analysis Patent data have long been one of the key ST&I sources for gaining CTI, and facilitating patent statistics, including unique attributes [e.g., international patent classification ...
doi:10.1109/tem.2021.3061862
fatcat:kudqaewka5hd5p5qvq3sqqvfgy
Simple and Effective Graph Autoencoders with One-Hop Linear Models
[article]
2020
arXiv
pre-print
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Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE) emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction ...
Graph AE, VAE and most of their extensions rely on multi-layer graph convolutional networks (GCN) encoders to learn vector space representations of nodes. ...
Existing models usually rely on graph neural networks (GNN) to encode nodes into embeddings. ...
arXiv:2001.07614v3
fatcat:bpe3e5ms7nbdxajpskmqxmpwza
A Comparison between Political Claims Analysis and Discourse Network Analysis: The Case of Software Patents in the European Union
2010
Social Science Research Network
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We compare discourse network analysis with political claims analysis, a competing method, and apply both methods to the European-level discourse on software patents. ...
It is based on social network analysis and qualitative content analysis and takes an entirely relational perspective. ...
The approach called discourse network analysis is compared with a competing method, political claims analysis, and both methods are applied to the European-level discourse on software patents. ...
doi:10.2139/ssrn.1617194
fatcat:xqc2g6vvljbxfoll52fa7qlv6m
A Degeneracy Framework for Scalable Graph Autoencoders
[article]
2019
arXiv
pre-print
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We evaluate and discuss our method on several variants of existing graph AE and VAE, providing the first application of these models to large graphs with up to millions of nodes and edges. ...
We achieve empirically competitive results w.r.t. several popular scalable node embedding methods, which emphasizes the relevance of pursuing further research towards more scalable graph AE and VAE. ...
Graph AE/VAE training All graph AE/VAE models were trained on 200 epochs to return 16-dim embeddings, except for Patent (500 epochs, 32dim). ...
arXiv:1902.08813v2
fatcat:xnw6my5ekrcadj7xay5az5qoaq
Trans4E: Link Prediction on Scholarly Knowledge Graphs
2021
Neurocomputing
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2021 pre-print arXiv:2107.03297v1 fatcat:wosvp4invfd33pzrce6ngtb6cu
In recent years, link prediction approaches based on Knowledge Graph Embedding models became the first aid for this issue. ...
Trans4E was applied on two large-scale knowledge graphs, the Academia/Industry DynAmics (AIDA) and Microsoft Academic Graph (MAG), for completing the information about Fields of Study (e.g., 'neural networks ...
Acknowledgements We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research. ...
doi:10.1016/j.neucom.2021.02.100
fatcat:j4iuey3rzreppow2zt3qnusjvm
AIDA: a Knowledge Graph about Research Dynamics in Academia and Industry
2021
Quantitative Science Studies
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We evaluated the different parts of the generation pipeline on a manually crafted gold standard yielding competitive results. ...
In this paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which describes 21M publications and 8M patents according to the research topics drawn from the Computer Science Ontology ...
We also thank Dimensions for sharing their large dataset of patents. ...
doi:10.1162/qss_a_00162
fatcat:diw25svrw5hlvddin4truogq3y
Information Retrieval and Visualization for Searching Scientific articles and Patents
2015
Research in Computing Science
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However, search is a humandriven activity and the result of such analysis is largely dependent on the initial inputs that are provided by the expert. ...
We present results of experiments based on research abstracts made available by digital libraries and US patent office. ...
Figure 5 presents a graph, each of whose nodes are topics that represent the area of Wireless Sensor Networks, which is the same as the topic-evolution graph component for the area, with one difference ...
doi:10.13053/rcs-90-1-21
fatcat:hsqovp6wrbaaziioy5a7xm4zxe
The Impact of Co-Inventor Networks on Smart Cleantech Innovation: The Case of Montreal Agglomeration
2021
Sustainability
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We use patent big data and apply a combination of network analysis techniques to explore the social structure of the Montreal tech community and its embeddedness in the global innovation landscape. ...
Our analysis reveals the importance of both local and international ties for the general development of innovations in Montreal's competitive urban economy, with a stronger impact of international ties ...
Citation Networks and Co-Inventor Networks The network analysis is based on the graphs defined by edges and vertices. Throughout the analysis we define a vertex to be a geolocated patent inventor. ...
doi:10.3390/su13137270
fatcat:gonmf354nzennp72doj5z7cksy
Network Analysis of Innovation in the Internet of Things
2018
Interdisciplinary Description of Complex Systems
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Conclusion: Drawing on an intellectual property database and employing social network analysis, this research quantifies the structure of innovation networks in terms of the results and operational efficiency ...
Method: In this research, the relationship between the network structure and the result of innovation was analysed through social network analysis. ...
In graph theory and network analysis, indicators of centrality identify the most important vertices within a graph. ...
doi:10.7906/indecs.16.2.2
fatcat:3taflcmwwzfnhnjl6eioihrnva
IP Analytics and Machine Learning Applied to Create Process Visualization Graphs for Chemical Utility Patents
2021
Processes
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A computer-supported graph-based knowledge representation interface is developed to plot the extracted chemical terms and their chemical process links as a network of nodes with connecting arcs. ...
The researcher must be able to understand the latest patents and literature in order to develop new chemicals and processes that do not infringe on existing claims and processes. ...
For easy analysis of complex network data and simulation models, NetworkX has algorithms for complex network analysis and graph visualization. ...
doi:10.3390/pr9081342
fatcat:fbh4kxol5vgidnv2qs7qa5pcxa
A NOVEL APPROACH FOR PATTERN RECOGNITION BY USING NETWORK AND THEORY OF COMPLEXITY
2018
Journal of Production Engineering
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In this article we present new method for pattern recognition and calculating all closeness centralization of network created with connection maximum value of 3D graph of hardenend specimens. ...
We use a method which combines an intelligent genetic algorithm and multiple regression to predict all closeness centralization of network created with connection maximum value of 3D graph of hardened ...
Since its inception twenty years ago, GP has been used to solve a wide range of practical problems, producing a number of human-competitive results and even patentable new inventions. ...
doi:10.24867/jpe-2018-01-021
fatcat:ee5jl2vtnvejpdz7l6wqgah3tm
Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach
[article]
2019
arXiv
pre-print
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In this paper, we focus on identifying nodes with high BC in a graph, since many application scenarios are built upon retrieving nodes with top-k BC. ...
Betweenness centrality (BC) is one of the most used centrality measures for network analysis, which seeks to describe the importance of nodes in a network in terms of the fraction of shortest paths that ...
Each edge connects an author to one of his publications. cit-Patents is a citation network of U.S. patents. Nodes are patents and edges represent citations. ...
arXiv:1905.10418v4
fatcat:m2p7mcwdmjfjbip2fjvzvzf5qe
Social Network Analysis of the Irish Biotech Industry: Implications for Digital Ecosystems
[chapter]
2010
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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The study applies social network analysis to determine the structure of networks of company directors and inventors in the biotech sector. ...
This paper presents an analysis of the socio-spatial structures of innovation, collaboration and knowledge flow among SMEs in the Irish biotech sector. ...
One established framework for analysing network structure is that of "small world" network analysis. Small world analysis is concerned with the density and reach of ties. ...
doi:10.1007/978-3-642-14859-0_3
fatcat:77vxtabxtjafpnhcefbgnsna44
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