عنوان انگلیسی مقاله:

Multi-layer graph analysis for dynamic social networks

ترجمه عنوان مقاله: تجزیه تحلیل نمودار چند لایه ای برای شبکه های اجتماعی پویا

رشته: فناوری اطلاعات

سال انتشار: 2014

تعداد صفحات مقاله انگلیسی: 10 صفحه

منبع: IEEE

نوع فایل: pdf

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چکیده مقاله

Modern social networks frequently encompass multiple distinct types of connectivity information; for instance, explicitly acknowledged friend relationships might complement behavioral measures that link users according to their actions or interests. One way to represent these networks is as multi-layer graphs, where each layer contains a unique set of edges over the same underlying vertices (users). Edges in different layers typically have related but distinct semantics; depending on the application multiple layers might be used to reduce noise through averaging, to perform multifaceted analyses, or a combination of the two. However, it is not obvious how to extend standard graph analysis techniques to the multi-layer setting in a flexible way. In this paper we develop latent variable models and methods for mining multi-layer networks for connectivity patterns based on noisy data.

Index Terms: Hypergraphs, multigraphs, mixture graphical models, Pareto optimality

مقدمه مقاله

Multi-layer networks arise naturally when there exists more than  one  source  of  connectivity  information  for  a  group  ofusers.  For  instance,  in  a  social  networking  context  there  is often knowledge of direct communication links, i.e., relational information.  Examples  of  relational  information  include  the frequency with which users communicate over social media, or  whether  a  user  has  sent  or  received  emails  from  another user  in  a  given  time  period.  However,  it  is  also  possible to  derive behavioral relationships  based  on  user  actions  or interests.  These  behavioral  relationships  are  inferred  from information  that  does  not  directly  connect  users,  such  as individual  preferences  or  usage  statistics.  In  this  paper  we show  how  to  deal  with  multiple  layers  of  a  social  network when performing tasks like inference, clustering, and anomaly detection.

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