已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Multilayer patent citation networks: A comprehensive analytical framework for studying explicit technological relationships

引用 计算机科学 数据科学 背景(考古学) 互补性(分子生物学) 元数据 万维网 地理 遗传学 考古 生物
作者
Kyle Higham,Martina Contisciani,Caterina De Bacco
出处
期刊:Technological Forecasting and Social Change [Elsevier]
卷期号:179: 121628-121628 被引量:13
标识
DOI:10.1016/j.techfore.2022.121628
摘要

• Patent citation networks are conceptually framed as multilayer networks, wherein nodes are patent families linked across jurisdictions. • Citation contexts, such as citing office and citation type, naturally form the layers within this network. • Layers are found to be complementary rather than redundant with respect to their technological information content. • Multilayer network communities are found to contain more nuanced and technologically-relevant information than their single-layer analogues. • Extending conventional patent citation networks to analytical settings that allow more comprehensive and realistic representations of technological relationships appears to be a promising avenue of research. The use of patent citation networks as research tools is becoming increasingly commonplace in the field of innovation studies. However, these networks rarely consider the contexts in which these citations are generated and are generally restricted to a single jurisdiction. Here, we propose and explore the use of a multilayer network framework that can naturally incorporate citation metadata and stretch across jurisdictions, allowing for a complete view of the global technological landscape that is accessible through patent data. Taking a conservative approach that links citation network layers through triadic patent families, we first observe that these layers contain complementary, rather than redundant, information about technological relationships. To probe the nature of this complementarity, we extract network communities from both the multilayer network and analogous single-layer networks, then directly compare their technological composition with established technological similarity networks. We find that while technologies are more splintered across communities in the multilayer case, the extracted communities match much more closely the established networks. We conclude that by capturing citation context, a multilayer representation of patent citation networks is, conceptually and empirically, better able to capture the significant nuance that exists in real technological relationships when compared to traditional, single-layer approaches. We suggest future avenues of research that take advantage of novel computational tools designed for use with multilayer networks.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
乐观的雪曼完成签到,获得积分20
1秒前
su完成签到 ,获得积分10
3秒前
敏感的机器猫完成签到,获得积分20
4秒前
7秒前
9秒前
何小熊发布了新的文献求助10
9秒前
李健应助火星上映易采纳,获得10
11秒前
月月发布了新的文献求助10
11秒前
14秒前
wrl2023发布了新的文献求助10
15秒前
15秒前
16秒前
天真豪英发布了新的文献求助10
21秒前
解青文发布了新的文献求助10
23秒前
rosalieshi应助NPC采纳,获得30
23秒前
开心的野狼完成签到 ,获得积分10
27秒前
28秒前
我是好人发布了新的文献求助10
29秒前
29秒前
yoga完成签到 ,获得积分10
31秒前
yar应助解青文采纳,获得10
31秒前
cc发布了新的文献求助10
34秒前
李健应助PPP采纳,获得10
40秒前
啊冰发布了新的文献求助10
46秒前
zjsu_zpz完成签到,获得积分10
50秒前
Ava应助qiu采纳,获得10
56秒前
傅荣轩完成签到,获得积分10
1分钟前
ding应助颜哈哈采纳,获得30
1分钟前
毛毛完成签到,获得积分10
1分钟前
1分钟前
啊冰关注了科研通微信公众号
1分钟前
1分钟前
辣味锅包肉发布了新的文献求助100
1分钟前
hdn完成签到 ,获得积分10
1分钟前
我是好人完成签到,获得积分10
1分钟前
Fx完成签到 ,获得积分10
1分钟前
1分钟前
1分钟前
善学以致用应助天天向上采纳,获得30
1分钟前
Luoling完成签到 ,获得积分10
1分钟前
高分求助中
Licensing Deals in Pharmaceuticals 2019-2024 3000
Cognitive Paradigms in Knowledge Organisation 2000
Effect of reactor temperature on FCC yield 2000
How Maoism Was Made: Reconstructing China, 1949-1965 800
Introduction to Spectroscopic Ellipsometry of Thin Film Materials Instrumentation, Data Analysis, and Applications 600
Promoting women's entrepreneurship in developing countries: the case of the world's largest women-owned community-based enterprise 500
Shining Light on the Dark Side of Personality 400
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3310963
求助须知:如何正确求助?哪些是违规求助? 2943728
关于积分的说明 8516304
捐赠科研通 2619056
什么是DOI,文献DOI怎么找? 1431863
科研通“疑难数据库(出版商)”最低求助积分说明 664484
邀请新用户注册赠送积分活动 649755