Degree-Degree Dependencies in Directed Networks with Heavy-Tailed Degrees
نویسندگان
چکیده
منابع مشابه
Degree-Degree Dependencies in Directed Networks with Heavy-Tailed Degrees
In network theory, Pearson’s correlation coefficients are most commonly used to measure the degree assortativity of a network. We investigate the behavior of these coefficients in the setting of directed networks with heavy-tailed degree sequences. We prove that for graphs where the inand out-degree sequences satisfy a power lawwith realistic parameters, Pearson’s correlation coefficients conve...
متن کاملDegree-degree correlations in directed networks with heavy-tailed degrees
In network theory, Pearson’s correlation coefficients are most commonly used to measure the degree assortativity of a network. We investigate the behavior of these coefficients in the setting of directed networks with heavy-tailed degree sequences. We prove that for graphs where the inand out-degree sequences satisfy a power law, Pearson’s correlation coefficients converge to a non-negative num...
متن کاملDegree-Degree Dependencies in Random Graphs with Heavy-Tailed Degrees
Mixing patterns in large self-organizing networks, such as the Internet, the World Wide Web, social, and biological networks are often characterized by degreedegree dependencies between neighboring nodes. In assortative networks, the degreedegree dependencies are positive (nodes with similar degrees tend to connect to each other), whereas in disassortative networks, these dependencies are negat...
متن کاملDegree-degree correlations in random graphs with heavy-tailed degrees
We investigate degree-degree correlations for scale-free graph sequences. The main conclusion of this paper is that the assortativity coefficient is not the appropriate way to describe degreedependences in scale-free random graphs. Indeed, we study the infinite volume limit of the assortativity coefficient, and show that this limit is always non-negative when the degrees have finite first but i...
متن کاملCollaborative Filtering and Heavy-Tailed Degree Distributions
Common techniques in collaborative filtering rely on finding low-rank matrix approximations to the adjacency matrix (ratings that users assign to items), essentially representing users and items as a collection of a small number of latent features. One issue that arises in many real world datasets for collaborative filtering is that the number of observed entries per row/column follows a heavy-...
متن کاملذخیره 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: Internet Mathematics
سال: 2014
ISSN: 1542-7951,1944-9488
DOI: 10.1080/15427951.2014.927038