Identifying influential nodes based on network representation learning in complex networks

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Identifying influential nodes in complex networks

Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational c...

متن کامل

Identifying influential nodes in complex networks with community structure

Article history: Received 2 July 2012 Received in revised form 14 January 2013 Accepted 16 January 2013 Available online 26 January 2013

متن کامل

Identifying influential spreaders in complex networks based on gravity formula

How to identify the influential spreaders in social networks is crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases and rumors, and so on. In this paper, by viewing the k-shell value of each node as its mass and the shortest path distance between two nodes as their distance, then inspired by the idea of the gravity formula, we propose a gr...

متن کامل

Locating influential nodes in complex networks

Understanding and controlling spreading processes in networks is an important topic with many diverse applications, including information dissemination, disease propagation and viral marketing. It is of crucial importance to identify which entities act as influential spreaders that can propagate information to a large portion of the network, in order to ensure efficient information diffusion, o...

متن کامل

Identifying influential spreaders in complex networks

Maksim Kitsak, 2 Lazaros K. Gallos, Shlomo Havlin, Fredrik Liljeros, Lev Muchnik, H. Eugene Stanley, and Hernán A. Makse Center for Polymer Studies and Physics Department, Boston University, Boston, Massachusetts 02215, USA Cooperative Association for Internet Data Analysis (CAIDA), University of California-San Diego, La Jolla, California 92093, USA Levich Institute and Physics Department, City...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: PLOS ONE

سال: 2018

ISSN: 1932-6203

DOI: 10.1371/journal.pone.0200091